Relevant bibliographies by topics / CHAID

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  2. Dissertations / Theses
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Academic literature on the topic 'CHAID'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "CHAID"

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Kagnicioglu, Celal Hakan, and Mune Mogol. "Implementation of Chaid Algorithm." International Journal of Research in Business and Social Science (2147-4478) 3, no. 4 (October 22, 2014): 42–51. http://dx.doi.org/10.20525/ijrbs.v3i4.116.

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Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM) tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID) algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.
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Park, Sung-Jae, Chang-Wook Lee, Saro Lee, and Moung-Jin Lee. "Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea." Remote Sensing 10, no. 10 (September 25, 2018): 1545. http://dx.doi.org/10.3390/rs10101545.

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We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.
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Santi, Vera Maya, Lina Nafisah, and Qorry Meidianingsih. "Penerapan Metode SMOTE CHAID dalam Klasifikasi Tuberkulosis Relapse." Jurnal Statistika dan Aplikasinya 6, no. 1 (June 30, 2022): 26–36. http://dx.doi.org/10.21009/jsa.06103.

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DKI Jakarta Province is one of the provinces with the highest tuberculosis cases, and a person's chance of contracting tuberculosis is the greatest among other provinces. Tuberculosis can be cured with regular treatment within a certain period of time, but after recovering, some tuberculosis sufferers may relapse so that it can cause new problems. This study aims to build a classification model and determine which factors influence tuberculosis relapse using the CHAID method. SMOTE with majority undersampling is applied as a solution to deal with the problem of patient categories (relapse and non-relapse) who have an unbalanced number of observations. Based on the CHAID classification tree, the results show that the factors that influence relapse in tuberculosis patients include the type of diagnosis, age, gender, and place of residence. In addition, the application of SMOTE can improve the performance of the CHAID classification tree in classifying patients based on their categories. These results were indicated by an increase in the values of accuracy, sensitivity, and specificity to 76,153; 26,667; and 82,608 compared to the performance of CHAID without SMOTE. Based on these results, the SMOTE CHAID classification model has better performance than CHAID
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Juwita, Puspa, Sugiman Sugiman, and Putriaji Hendikawati. "Ketepatan Klasifikasi Metode Regresi Logistik dan Metode Chaid dengan Pembobotan Sampel." Indonesian Journal of Mathematics and Natural Sciences 44, no. 1 (April 12, 2021): 22–33. http://dx.doi.org/10.15294/ijmns.v44i1.32699.

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Tujuan penelitian ini adalah menentukan ketepatan metode regresi logistik dan CHAID dengan pembobotan sampel pada klasifikasi status angkatan kerja Kabupaten Temanggung 2015. Populasi dalam penelitian ini adalah angkatan kerja Kabupaten Temanggung 2015. Data dalam penelitian ini diperoleh dari Sakernas Kabupaten Temanggung 2015. Variabel dependen dalam penelitian ini adalah angkatan kerja, sedangkan variabel independennya adalah klasifikasi desa/kelurahan, hubungan dengan kepala rumah tangga, jenis kelamin, umur, status pernikahan, pendidikan, pelatihan kerja, dan pengalaman kerja. Dari analisis regresi logistik diperoleh persamaan, sedangkan anlalisi CHAID menghasilkan pohon klasifikasi. Persamaan dan pohon klasifikasi tersebut dapat digunakan untuk memprediksi variabel dependen. Kesalahan klasifikasi dihitung menggunakan APER (Apparent Error Rate), kemudian ketepatan klasifikasi dapat diperoleh dengan rumus 1 – APER. Ketepatan regresi logistik dan CHAID dengan pembobotan sampel secara berturut-turut adalah 96,4% dan 96,6%. Hal ini menunjukkan ketepatan metode CHAID pada klasifikasi status angkatan kerja Kabupaten Temanggung 2015 lebih tinggi dibandingkan regresi logistik.The purpose of this study is to determine the accuracy of logistic regression and CHAID with sample weighting on Temanggung regency labor status classification in 2015. The population of this study is labor of Temanggung Regency in 2015. The data of this study is obtained from Sakernas of Temanggung Regency in 2015. The dependent variable of this study is labor status, whereas the independent variables of this study are domicile region, relation with family head, gender, age, marriage status, education level, job training, and job experience. Logistic regression analysis results a mathematic equation, and CHAID method result a classification tree. Those result can predict the dependent variable. Classification error is calculated using APER (Apparent Error Rate), then the accuracy can be calculated by 1- APER. Accuracy of logistic regression and CHAID with sample weighting respectively are 96,4% and 96,6%. This show that accuracy of CHAID is greater than logistic regression.
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Fitrianto, Anwar, Wan Zuki Azman Wan Muhamad, and Budi Susetyo. "Development of direct marketing strategy for banking industry: The use of a Chi-squared Automatic Interaction Detector (CHAID) in deposit subscription classification." Journal of Socioeconomics and Development 5, no. 1 (February 25, 2022): 64. http://dx.doi.org/10.31328/jsed.v5i1.3420.

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A comparison between Chi-squared Automatic Interaction Detector (CHAID) and logistic regression analysis was performed for classification problems on bank direct marketing data. CHAID Performance Comparison and comparison with Logistic Regression (LR) performance were also conducted. Priority performance with two statistical measures was evaluated: classification accuracy and sensitivity in the presence of data containing categorical imbalances. Random over sampling (ROS) was then applied to deal with class balance problems to get better performance of CHAID analysis. Segmentation analysis was also performed using the CHAID approach to improve the performance of the analysis results. CHAID outperforms LR because of its advantages that it can be used to perform segmentation modeling. Direct marketers should pay attention to traits are Duration, Month, Contact, and Housing. To get a higher subscription, the bank must extend the call duration. Based on these results, the banking industry needs to prepare regulations related to human resources, infrastructure, costs, and government support to achieve higher subscriptions.JEL Classification A10; C10; G21
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Ploquin, Anne, David Olmos, Denis A. Lacombe, Roger A'Hern, Alain Duhamel, Christopher Twelves, Silvia Marsoni, et al. "Prediction of early death among patients (pts) enrolled in phase I trials: Development and validation of a new model based on platelet count and albumin level." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 2540. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.2540.

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2540 Background: Selecting pts with “sufficient life expectancy” for phase I oncology trials remains challenging. The Royal Marsden Model (RMM; Arkenau, JCO 2009) identified high-risk pts as those with ≥2 of: albumin (ALB) <35g/l; LDH >ULN; more than 2 metastatic sites. The RMM was assessed in 1845 pts treated in phase I trials by members of the European New Drug Development Network (ENDDN). The present study aimed to develop an alternative prognostic model using a different methodology and to compare its performance with the RMM. Methods: The primary endpoint was 90-day mortality rate (90-DMR). The new model was developed from the ENDDN database using CHAID, an exploratory non-parametric data analysis method evaluating the relationship between a dependent variable (90-DMR) and possible predictive variables through a decision-tree analysis. The ROC characteristics and calibration of both methods were then validated in the independent EORTC Database (n=341 pts). Results: The CHAID method identified low and high-risk groups with 90-DMR of 9.5% vs 37.5%. High-risk pts had ALB<33g/L or ALB≥33g/L but platelet count ≥400.000/mm3. Applying both models to the validation dataset; the rates of correctly-classified pts were 0.86 [CI-95% 0.82-0.90] vs. 0.67 [CI-95% 0.60-0.74], with the CHAID model and RMM respectively. The CHAID model had higher specificity (0.90 vs. 0.65), but lower sensitivity (0.36 vs. 0.93) than the RMM. Discriminative slopes were similar for the CHAID model and RMM (19.4% and 19.3%, respectively). The negative predictive value (NPV; correct identification of pts surviving 90 days) was similar for the CHAID model and RMM (0.94 [0.91-0.97] and 0.99 [0.94-1.00] respectively). Calibration, assessed by the Brier score, was slightly better with the CHAID model (0.001 and 0.098, respectively). Conclusions: In selecting pts for phase I trials arguably an important criterion is NPV; the CHAID model and RMM provided a similar high level of NPV but the CHAID model gave a better rate of correctly-classified patients. Both models improved the screening process and reduce the attrition rate in phase I trials.
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Sa'diah, Chalimatus, Tatik Widiharih, and Arief Rachman Hakim. "KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X)." Jurnal Gaussian 10, no. 2 (May 31, 2021): 159–69. http://dx.doi.org/10.14710/j.gauss.v10i2.29923.

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One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.
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FAIZA, NUR, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "METODE QUEST DAN CHAID PADA KLASIFIKASI KARAKTERISTIK NASABAH KREDIT." E-Jurnal Matematika 4, no. 4 (November 24, 2015): 163. http://dx.doi.org/10.24843/mtk.2015.v04.i04.p106.

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This aim of this research is to find out the classification results and to compare the magnitude of misclassification of QUEST and CHAID methods on the classification of customer of Adira Kredit Elektronik branch Denpasar. QUEST (Quick, Unbiased, Efficient Statistical Trees) and CHAID (Chi-squared Automatic Interaction Detection) are nonparametric methods that produce tree diagram which is easy to interpret. The QUEST and CHAID classification methods conclude that: 1) QUEST method produces three groups which predict customers into the current category, whereas CHAID method produces four groups which also predict customer into the current category; 2) both methods generate the biggest classification accuracy for customers that current category which share similar characteristics; 3) both methods also have the same degree of accuracy in classifying customer data Adira Kredit Elektronik branch Denpasar.
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Khodijatunnuriyah, Siti, and Hasih Pratiwi. "Klasifikasi Jenis Pencabutan Layanan oleh Pelanggan Indihome Menggunakan Metode Chi-Square Automatic Interaction Detection." Indonesian Journal of Applied Statistics 2, no. 2 (December 27, 2019): 80. http://dx.doi.org/10.13057/ijas.v2i2.34526.

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<p>Market segmentation is a classic topic in marketing which is never loss its attractiveness. In addition to market segmentation, customer satisfaction is important in the field of marketing. Customer satisfaction is a person's feelings after using goods or services produced by a company. High customer satisfaction shows a company's success in producing goods or services. Statistics provides many tools for segmentation research. One of statistical tool for segmentation research which takes the dependency method as an approach is Chi-Squared Automatic Interaction Detection (CHAID) analysis. CHAID analysis would provide decision tree like diagram which provide information about degree of association from dependent variable to the independent variables and the information about segments characteristic. In this case, the CHAID analysis is used to determine the type of service revocation segmentation by Indihome customers. Based on CHAID analysis, 25 segmentations were obtained, which consisted of revocation of the downgrade category of 45314 customers and the number of revocation of the Churn Out category by 11137 customers.</p><strong>Keywords : </strong>market segmentation, customer satisfaction<strong>, </strong>CHAID, Indihome
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Abbas, Ansar, Muhammad Aman Ullah, and Abdul Waheed. "Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 2 (December 24, 2021): 29–38. http://dx.doi.org/10.53560/ppasa(58-2)603.

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This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL > 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.
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Dissertations / Theses on the topic "CHAID"

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Cadiz, Horacio T. "The development of a CHAID-based model for CHITRA93." Master's thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-04272010-020155/.

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ESTRADA, Gabriela del Carmen Calderón. "Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala." Universidade Federal Rural de Pernambuco, 2015. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6067.

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Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq
The rust, caused by the fungus Hemileia vastatrix Berk & Br., is the main disease of coffee (Coffea arabica L.) in Latin America. The principal damage caused is defoliation and death of lateral branches, which causes premature fruit losses. Guatemala produces coffee in 270,000 hectares, and near of the 82% is cultivated with susceptible varieties to coffee rust races. Coffee rust epidemic is a complex process based on the relationships between the environment, plant growth, and crop practices. The objective of this study was to develop models for risk analysis based on decision trees in order to understand how cropping patterns determine the progress of the disease in Guatemala to identify and prioritize the important factors. For this work were used 1215 observations, obtained in 35 coffee plots from April 2013 to December 2014. The modeled variable was the leaf severity. Using the CHAID (Chi-Square Automatic Interaction Detection) algorithm were developed two decision trees. The first predicts leaf severity in plots where the producer does not follow the disease, while the second requires rust monitoring 28 days before the date of the severity risk analysis. In the trees, the main predictor was the fungicide spraying per year. The following predictor variables on the tree were related with the tissue availability for new infections, which also stimulates microenvironments with high relative humidity, warm temperatures, and foliar wetness prevalence. Only for non-monitoring tree was included the average rainfall, which suggests that climate relationship with the epidemic, is at microclimate level. The tree for plots with disease monitoring includes in all levels the 28 before severity and replaced management or climate variables getting similar predicted values. The accuracy of the tree for monitored plots was 65.85% with an estimated accuracy by cross validation of 73.34%, and for the monitored plots, the accuracy was 62.53% and 68.54%, respectively. Risk analysis models prove to be tools of support in making management decisions to implement the control of coffee rust and allow list in order of importance, management practices, and climatic factors that influence disease severity in different crop patterns.
A ferrugem do cafeeiro, causada pelo fungo Hemileia vastatrix Berk & Br., é a principal doença do cafeeiro (Coffea arabica L.) na América Latina. O principal dano é desfolha e morte de ramos laterais, que provocam perdas prematuras de frutos. A Guatemala produz café em 270.000 hectares, sendo que cerca de 82% é cultivado com variedades suscetíveis às raças de ferrugem. A epidemia da ferrugem é um processo complexo baseado nas relações entre ambiente, crescimento da planta, e práticas de manejo. O objetivo deste estudo foi desenvolver modelos para análise de risco baseados em árvores de decisão, a fim de entender como os padrões de cultivo determinam o progresso da doença na Guatemala para identificae e priorizar os fatores importantes. Para este trabalho foram utilizadas 1215 observações, obtidas de 35 lavouras de abril de 2013 a dezembro de 2014. A variável modelada foi a severidade da folha. Utilizando o algoritmo CHAID (Chi-Quadrado Detecção Automatic Interaction), foram desenvolvidas duas árvores de decisão. A primeira árvore permite prever a severidade na folha nas parcelas em que o produtor não realiza acompanhamento da doença, enquanto a segunda requer o monitoramento da ferrugem 28 dias antes da data da análise de risco da severidade. Nas árvores, o principal preditor foi o número de aplicações de fungicida por ano. As seguintes variáveis preditoras na árvore foram relacionadas com disponibilidade de tecido para novas infecções, que podem favorecem a formação de microambientes com alta umidade relativa, temperaturas amenas e prevalência da molhadura folhar. Apenas para a árvore de não monitoramento foi incluída a variável da precipitação média, o que sugere que a relação do clima é em nível microclimático. A árvore com monitoramento inclui em todos os níveis a severidade aos 28 dias antes e substitui variáveis de manejo ou clima, estimando valores semelhantes. A acurácia da árvore para lavouras não monitoradas foi de 65,85% com uma estimativa de acurácia por validação cruzada de 73,34%. Na árvore para lavouras monitoradas a acurácia foi de 62,53% e 68,54%, respectivamente. Os modelos de análise de risco demonstram ser ferramentas de apoio na tomada de decisões de manejo para implementar o controle da ferrugem do cafeeiro e possibilitam listar, em ordem de importância, as práticas de manejo e fatores climáticos que influenciam na severidade da doença em diferentes padrões do cultivo.
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Herrera, Conislla Diana Marisol. "Técnica de segmentación jerárquica Chaid de clientes para otorgamiento de créditos financieros." Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2016. https://hdl.handle.net/20.500.12672/6120.

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Desarrolla la técnica de segmentación jerárquica CHAID para determinar la clasificación y predicción en la evaluación del futuro cliente para disminuir los riesgos de morosidad. Para su aplicación, se estudia la clasificación según riesgo crediticio de clientes de una financiera de crédito que trabaja con miembros de la fuerza aérea del Perú. Este tipo de clientes tiene ciertas particularidades que los diferencia de otros ya que se trata de una población con ingresos fijos mediante planilla y con posibilidades de evaluación crediticia real, a pesar de ello, se observa como problema, la presencia de clientes morosos.
Trabajo de investigación
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Pasupathy, Kalyan Sunder. "Sustainability of the Service-Profit Chain." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/26257.

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Managers in organizations make investment decisions all the time. These decisions have an impact on the bottom-line profits and on the market penetration of the organization. Some decisions have more impact than others do and not all such decisions are evaluated for their impact. The Service-Profit Chain (SPC) framework brings together several components like operational attributes, customer perceptions, customer behavioral intentions and customer loyalty to evaluate the service operation. This research augments the SPC with another component â uncontrollable factors (environmental variables and competition) that are exogenous to the operation but definitely have an effect on the service delivery process. Further, this research develops a dynamic model to evaluate investments made in operational attributes (e.g. number of tellers in a bank, number of airline flight options to a particular city available to customers) and determine the behavior of customer perceptions, customer intentions, customer loyalty, profit, market penetration and marginal rate of return over time. The above is accomplished by incorporating a hill-climbing algorithm into the dynamic SPC model. This hill-climbing algorithm senses the current state of the system and compares it to a certain goal to determine the discrepancies and make additional interventions. The objective is to determine an optimal path to steady state and to evaluate if certain goals are realistic. Next, the Service Sustainability Chain is developed to be applicable to training services. This is accomplished by building key relationships specific to training services into separate modules. The Dynamic SPC module is based on the SPC framework. The Customer Base Growth module captures the structure for referrals and how this enables the growth of the customer base mimicking the infectious model for epidemic diseases in the literature. A methodology based on Chi-Square Automatic Interaction Detection (CHAID) and Structural Equation Modeling (SEM) developed to explore, uncover and identify relationships and mathematical equations is used to determine the structural input-output representation of the SPC. Next, the model and the methodology developed are applied to a case study in a training services organization, simulated and validated.
Ph. D.
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Panzera, Anthony Dominic. "Understanding Factors Determining Early Termination from a Government Assistance Program for Maternal and Child Health: The Special Supplemental Nutrition Program for Women, Infants and Children (WIC)." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5616.

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The purpose of this dissertation is to understand why individuals enrolled in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) fail to retrieve food vouchers, miss WIC appointments, and become inactive in program components. In Kentucky, mothers who fail to pick up food instruments for 60 days are automatically terminated from the program. The specific research questions that guided this study are: (1) Which segments of enrollees are at greatest and least risks of nonparticipation in the WIC program? (2) How do predisposing, enabling and need characteristics impact WIC nonparticipation among eligible mothers? (3) How do WIC enrollees describe their experiences using WIC? (4) What do WIC enrollees report as reasons for nonparticipation while still eligible? Addressing these research questions will inform the development of practical outreach solutions specifically tailored for the purpose of mitigating nonparticipation in WIC and contribute to our understanding of the factors that deter eligible families from using government assistance programs like WIC.
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Fernandes, Fabiano Rodrigues. "Emprego de diferentes algoritmos de árvores de decisão na classificação da atividade celular in vitro para tratamentos de superfícies de titânio." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/165456.

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O interesse pela área de análise e caracterização de materiais biomédicos cresce, devido a necessidade de selecionar de forma adequada, o material a ser utilizado. Dependendo das condições em que o material será submetido, a caracterização poderá abranger a avaliação de propriedades mecânicas, elétricas, bioatividade, imunogenicidade, eletrônicas, magnéticas, ópticas, químicas e térmicas. A literatura relata o emprego da técnica de árvores de decisão, utilizando os algoritmos SimpleCart(CART) e J48, para classificação de base de dados (dataset), gerada a partir de resultados de artigos científicos. Esse estudo foi realizado afim de identificar características superficiais que otimizassem a atividade celular. Para isso, avaliou-se, a partir de artigos publicados, o efeito de tratamento de superfície do titânio na atividade celular in vitro (células MC3TE-E1). Ficou constatado que, o emprego do algoritmo SimpleCart proporcionou uma melhor resposta em relação ao algoritmo J48. Nesse contexto, o presente trabalho tem como objetivo aplicar, para esse mesmo estudo, os algoritmos CHAID (Chi-square iteration automatic detection) e CHAID Exaustivo, comparando com os resultados obtidos com o emprego do algoritmo SimpleCart. A validação dos resultados, mostraram que o algoritmo CHAID Exaustivo obteve o melhor resultado em comparação ao algoritmo CHAID, obtendo uma estimativa de acerto de 75,9% contra 58,6% respectivamente, e um erro padrão de 7,9% contra 9,1% respectivamente, enquanto que, o algoritmo já testado na literatura SimpleCart(CART) teve como resultado 34,5% de estimativa de acerto com um erro padrão de 8,8%. Com relação aos tempos de execução apurados sobre 22 mil registros, evidenciaram que o algoritmo CHAID Exaustivo apresentou os melhores tempos, com ganho de 0,02 segundos sobre o algoritmo CHAID e 14,45 segundos sobre o algoritmo SimpleCart(CART).
The interest for the area of analysis and characterization of biomedical materials as the need for selecting the adequate material to be used increases. However, depending on the conditions to which materials are submitted, characterization may involve the evaluation of mechanical, electrical, optical, chemical and thermal properties besides bioactivity and immunogenicity. Literature review shows the application decision trees, using SimpleCart(CART) and J48 algorithms, to classify the dataset, which is generated from the results of scientific articles. Therefore the objective of this study was to identify surface characteristics that optimizes the cellular activity. Based on published articles, the effect of the surface treatment of titanium on the in vitro cells (MC3TE-E1 cells) was evaluated. It was found that applying SimpleCart algorithm gives better results than the J48. In this sense, the present study has the objective to apply the CHAID (Chi-square iteration automatic detection) algorithm and Exhaustive CHAID to the surveyed data, and compare the results obtained with the application of SimpleCart algorithm. The validation of the results showed that the Exhaustive CHAID obtained better results comparing to CHAID algorithm, obtaining 75.9 % of accurate estimation against 58.5%, respectively, while the standard error was 7.9% against 9.1%, respectively. Comparing the obtained results with SimpleCart(CART) results which had already been tested and presented in the literature, the results for accurate estimation was 34.5% and the standard error 8.8%. In relation to execution time found through the 22.000 registers, it showed that the algorithm Exhaustive CHAID presented the best times, with a gain of 0.02 seconds over the CHAID algorithm and 14.45 seconds over the SimpleCart(CART) algorithm.
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Miller, Brian. "Development of a Chaid Decision Tree for Assessing Risk of Detecting Metabolic Syndrome in Adults, Age 20-39 Years." University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1342752599.

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Alfonso, Moya L. "The Tip of the Blade: Self-Injury Among Early Adolescents." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002096.

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Rice, Homer J. "Before the Storm: Evacuation Intention and Audience Segmentation." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3604.

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The purpose of this study was to describe the predictors of evacuation intention among coastal residents in the State of Florida and to determine if there are meaningful segments of the population who intend to evacuate when told to do so by governmental officials because of a major hurricane. In the America’s and the Caribbean, 75,000 deaths have been attributed to hurricanes in the 20 th century. A well planned evacuation can reduce injury and death, yet many people do not have an evacuation plan and do not intend to evacuate when told to do so. The study used secondary data from the Harvard School of Public Health, Hurricane in High Risk Areas study, a random sample of 5,046 non-institutionalized persons age 18 and older in coastal counties of Texas, Louisiana, Mississippi, Alabama, Georgia, North Carolina, South Carolina and Florida. Surveys for the State of Florida were segregated and used in this analysis, resulting in a study sample of 1,006 surveys from 42 counties. When asked if they would evacuate in the future if told to by government officials, 59.1% of Floridians surveyed said they would leave, 35.2% said they would not leave and 5.6% said it would depend. In Florida, 65.7% of the population had been threatened or hit by a major hurricane in the last three years and 26.6% of those had left their homes because of the hurricane. Of those whose communities were threatened by a hurricane, 83.3% of the communities were damaged and 33.8% experienced major flooding associated with the hurricane. Bivariate statistics and logistic regression were used to explore the interactions of predictors and evacuation intention. The best predictor of evacuation intention was prior evacuation from a hurricane (chi-square= 45.48, p < .01, Cramer’s V = 0.266). Significant relationships were also demonstrated between evacuation intention and worry a future hurricane would hit the community (chi-square = 22.75, p < .01, Cramer’s V = 0.11), the presence of pets (chi-square = 6.57, p < .01, Cramer’s V = 0.084), concern the home would be damaged (chi-square = 19.41, p < .01, Cramer’s V = 0.10), belief the home would withstand a major hurricane (chi-square = 19.55, p < .01, Cramer’s V = 0.10), length of time in the community (chi-square = 26.59, p < .01, Cramer’s V = 0.12), having children in the household (chi-square = 11.13, p < .01, Cramer’s V = 0.11), having a generator (chi-square = 17.12, p < .01, Cramer’s V = 0.13), age (chi-square = 24, p < .01, Cramer’s V = 0.16) and race (chi-square = 12.21, p = .02, Cramer’s V = 0.12). Logistic regression of the predictors of evacuation intention resulted in significant relationships with previous evacuation experience (OR = 4.99, p < .001), age 30 to 49 compared to age over 65 (OR = 2.776, p < .01), the presence of a generator (OR = .447, p < .01), having a home not very likely to be damaged compared to a home very likely to be damaged (OR =.444, p = .018), and experiencing poor prior government and voluntary agency response to previous hurricanes compared to excellent response (OR = .386, p < .027). Chi-squared Automatic Interaction Detection (CHAID) was used to identify segments of the population most likely and least likely to evacuate when told to do so. Those most likely to evacuate had evacuated due to a previous hurricane. Those least likely to evacuate when told to do so had not evacuated in a previous storm, do not own a generator and are over the age of 65. Information from this study can be used in planning for evacuation response by governmental entities. Available demographic information can be used to determine numbers of persons likely to evacuate before a storm. The results of this study can be used to inform a marketing strategy by government officials to encourage evacuation among those who say they would not evacuate when told to do so. Further research is needed to determine additional characteristics of the populations who say they will and will not evacuate when told to do so.
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Sebastiao, Yuri Combo Vanda. "Racial and Ethnic Differences in Low-Risk Cesarean Deliveries in Florida." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6583.

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Background and Significance: Cesarean delivery rates increased by more than 50% between 1996 and 2011 in the United States. The large increase in rates for the procedure was generally not associated with significant improvements in obstetric outcomes, raising concern about quality and prompting recommendations for prevention. Primary cesareans provide the best opportunity to reduce overall cesarean rates, and the group of first-time mothers considered low-risk for cesarean (known as nulliparous, term, singleton, vertex, NTSV) constitutes the focus of prevention efforts. Studies increasingly report racial and ethnic differences in NTSV cesareans, which remain after controlling for health factors. However, the reasons for these disparities and whether or not they can be mitigated are issues that are not well known. The objective of this investigation was to examine factors that modify the association between race, ethnicity and NTSV cesarean deliveries in Florida. Our overall aim was to improve understanding of drivers of racial and ethnic disparities in cesareans in order to inform efforts to reduce disparities. Methods: We conducted a population-based retrospective cohort study of 145,117 NTSV deliveries in labor, using Florida’s linked birth certificate and maternal hospital discharge records for the period of 2012 to 2014. The study was restricted to births in routine delivery hospitals to five racial and ethnic groups: non-Hispanic whites and blacks (including Haitians), Cubans, Puerto Ricans, and Mexicans. Two contrasting approaches were employed in the analysis. First, generalized linear mixed modelling was used to examine, quantify and describe effect modification of the race/ethnicity–association by cesarean risk factors. Non-Hispanic whites were the reference group for comparison. Second, classification tree modeling (chi-Squared Automatic Interaction Detection, CHAID) was used to identify cesarean risk factor combinations that define distinct subgroups with high and low rates of NTSV cesarean among the different racial and ethnic groups in the study population. Risk factors examined included individual socioeconomic, medical and health service-related factors, hospital factors, and a maternal neighborhood index of deprivation/affluence. Results: Non-Hispanic whites were the largest racial/ethnic group in the study population (57.6%), followed by non-Hispanic blacks (23%), Cubans (8.1%), Puerto Ricans (6.8%) and finally Mexicans (4.5%). All four minority groups experienced a higher risk of cesarean relative to non-Hispanic whites after adjusting for significant risk factors, with Cubans having the highest adjusted risk ratio (RR, 1.27) followed by non-Hispanic blacks (RR, 1.18). From the regression-based tests of effect modification, we found positive interactions between race (non-Hispanic black versus white), older gestational age, and labor induction; and negative interactions between ethnicity (Cuban versus non-Hispanic white), presence of medical risk conditions, and labor induction. The adjusted RR of cesarean comparing blacks to whites was 1.04 among spontaneous deliveries at early term (P=.33), but increased to 1.28 (P Conclusions: Our findings on risk factors that modified the association between race, ethnicity and NTSV cesarean delivery and differences in cesarean risk subgroups between racial and ethnic groups suggest that there are potential opportunities to reduce disparities in rates for the procedure in Florida. Whereas racial disparities appear to be related to disparities in health service factors related to cesarean, ethnic disparities appear to persist above and beyond the medical and health service factors included in this investigation. Further research, potentially involving qualitative methods and targeting some of the identified maternal subgroups with high rates of cesarean may help clarify maternal cultural factors, or differences in patient-provider interaction, that may contribute to some of the disparities.
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Books on the topic "CHAID"

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Magidson, Jay. SSPS for Windows CHAID: Release 6.0. Chicago,Ill: SPSS, 1993.

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Chaud, chaud, chaud. Montréal: La Courte échelle, 2007.

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Widess, Jim. The complete guide to chair caning: Restoring cane, rush, splint, wicker, and rattan furniture. New York, N.Y: Lark Books, 2006.

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Michel, Rouleau, ed. Chaud, chaud, le Pôle Nord!: Roman. Saint-Laurent, Québec: Éditions P. Tisseyre, 2006.

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Greenblatt, Miriam. Chad. New York: Children's Press, 1996.

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Sevigny, Joseph A. Chad. Washington, D.C: American Association of Collegiate Registrars and Admissions Officers, 1995.

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Whiteman, Kaye. Chad. London: Minority Rights Group, 1988.

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Chad. Kyïv: Avtohraf, 2008.

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Joffé, George. Chad. Oxford, Eng: Clio Press, 1995.

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Kneib, Martha. Chad. New York: Marshall Cavendish Benchmark, 2007.

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Book chapters on the topic "CHAID"

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Maass, Rüdiger, and Lutz Vetter. "CHAID — Chisquare Automatic Interaction Detection." In Neuere statistische Verfahren und Modellbildung in der Geoökologie, 95–101. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-83735-6_6.

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Schröder, Winfried. "CHAID-Analyse des Bedingungsgefüges von Waldschäden." In Neuere statistische Verfahren und Modellbildung in der Geoökologie, 195–223. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-83735-6_13.

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Musiol, Gerald, and Guido Steinkamp. "CHAID: Ein Instrument für die empirische Marketingforschung." In Computer Based Marketing, 581–90. Wiesbaden: Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-663-11996-8_60.

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Musiol, Gerald, and Guido Steinkamp. "CHAID: Ein Instrument für die empirische Marketingforschung." In Computer Based Marketing, 581–90. Wiesbaden: Vieweg+Teubner Verlag, 1998. http://dx.doi.org/10.1007/978-3-322-91958-8_60.

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Aggarwal, Udit, Sai Sabitha, Tanupriya Choudhury, and Abhay Bansal. "Indian Stock Market Analysis Using CHAID Regression Tree." In Advances in Intelligent Systems and Computing, 533–52. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3223-3_52.

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Glăvan, Ionela Roxana, Daniel Petcu, and Emil Simion. "CART Versus CHAID Behavioral Biometric Parameter Segmentation Analysis." In Innovative Security Solutions for Information Technology and Communications, 59–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27179-8_5.

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Boire, Richard. "Value-Based Segmentation and the Use of CHAID." In Data Mining for Managers, 143–49. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_18.

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Gupta, Rajan, and Saibal K. Pal. "Click-Through Rate Estimation Using CHAID Classification Tree Model." In Advances in Analytics and Applications, 45–58. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1208-3_5.

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Felea, M. G., V. Felea, and C. M. Gavrilescu. "Using CHAID Algorithm in Low-Risk Metabolic Syndrome Patients." In 3rd International Conference on Nanotechnologies and Biomedical Engineering, 466–69. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-736-9_110.

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du Toit, S. H. C., A. G. W. Steyn, and R. H. Stumpf. "CHAID and XAID: Exploratory Techniques for Analyzing Extensive Data Sets." In Springer Texts in Statistics, 224–44. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4950-4_8.

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Conference papers on the topic "CHAID"

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Ozgulbas, Nermin, and Ali Serhan Koyuncugil. "Developing Road Maps for Financial Decision Making by CHAID Decision Tree: CHAID Decision Tree Application." In 2009 International Conference on Information Management and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icime.2009.135.

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Belaïd, A., T. Moinel, and Y. Rangoni. "Improved CHAID algorithm for document structure modelling." In IS&T/SPIE Electronic Imaging, edited by Laurence Likforman-Sulem and Gady Agam. SPIE, 2010. http://dx.doi.org/10.1117/12.839794.

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Sari, Fitri Mudia, Rahmad Fadhillah, Asih Yuhesty, Siti Hariksa, Iva Agustina Sari, and Irene Simanungkalit. "Public Transportation Users Segmentation Using CHAID Method." In Proceedings of the 2nd International Conference on Mathematics and Mathematics Education 2018 (ICM2E 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icm2e-18.2018.8.

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Tang, Kuo-Tai, and Chih-Hao Chang. "CHAID algorithm to analyze characteristics of take-out industry." In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021. http://dx.doi.org/10.1109/iccece51280.2021.9342345.

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"Predicting Antenatal Care Utilization in the Philippines: A CHAID Analysis." In Multi-Disciplinary Manila (Philippines) Conferences Jan. 26-27, 2017 Cebu (Philippines). Universal Researchers (UAE), 2017. http://dx.doi.org/10.17758/uruae.uh0117416.

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Elsayad, Alaa M., Mujahed Al-Dhaifallah, and Ahmed M. Nassef. "Analysis and Diagnosis of Erythemato-Squamous Diseases Using CHAID Decision Trees." In 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2018. http://dx.doi.org/10.1109/ssd.2018.8570553.

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Kuranova, Pavlina. "Evaluation of the Phadiatop test results using CHAID algorithm and logistic regression." In 2015 International Conference on Information and Digital Technologies (IDT). IEEE, 2015. http://dx.doi.org/10.1109/dt.2015.7222969.

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Coskun, Mahmut, and Halil Ibrahim Bulbul. "Investigation of Factors Affecting Ownership the Household Informatics Equipment with CHAID Algorithm." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00326.

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Zhao, Mingqi, and Zhijun Sun. "Research on CHAID Decision Tree Model Based on Rating of China's Small Enterprises." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2263.

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Sano, Albert Verasius Dian, Adriel Anderson Stefanus, Elizabeth Paskahlia Gunawan, Choirul Huda, and Chasandra Puspitasari. "How CHAID-Based Rule Induction Algorithm Helps Managements of Tourism Sites Improve Tourists' Experiences." In 2021 International Conference on Information Management and Technology (ICIMTech). IEEE, 2021. http://dx.doi.org/10.1109/icimtech53080.2021.9535026.

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Reports on the topic "CHAID"

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Hernandez, Ricardo, Ben Belton, Thomas Reardon, Chaoran Hu, Xiaobo Zhang, and Akhter Ahmed. Value chain transformation. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/9780896293618_03.

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Grayson, Nakia R. Supply Chain Assurance:. Gaithersburg, MD: National Institute of Standards and Technology, 2022. http://dx.doi.org/10.6028/nist.sp.1800-34.

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Wagener, Kenneth, Hector Zuluaga, and Paula Delgado. Polycarbosilane Elastomers via Chain-Internal and Chain-End Latent Crosslinking. Fort Belvoir, VA: Defense Technical Information Center, August 2007. http://dx.doi.org/10.21236/ada474165.

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Jaboln, Sara. Chai Life. Ames: Iowa State University, Digital Repository, 2014. http://dx.doi.org/10.31274/itaa_proceedings-180814-978.

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Dijkxhoorn, Youri, Christine Plaisier, Coen van Wagenberg, Tim Verwaart, Jos Verstegen, Ruerd Ruben, and Ruben Oldenhof. Value chain laboratory : alternative evaluation method for assessing value chain dynamics. Wageningen: Wageningen Eonomic Research, 2017. http://dx.doi.org/10.18174/420482.

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Coughlin, Cletus C., Patricia S. Pollard, and Jerram C. Betts. To Chain or Not to Chain Trade-Weighted Exchange Rate Indexes. Federal Reserve Bank of St. Louis, 1996. http://dx.doi.org/10.20955/wp.1996.010.

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Marshak, Ronni. Supporting the Customer Chain. Boston, MA: Patricia Seybold Group, April 2010. http://dx.doi.org/10.1571/psgp04-08-10cc.

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Iyer, Ananth, Ahmed Soliman, and Amanda Thompson. Indiana Furniture Supply Chain. West Lafayette, IN: Purdue University, 2006. http://dx.doi.org/10.5703/1288284313373.

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Fussell, Z., K. Olson, and S. Patra. nEXO Passive Signal Chain. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1798443.

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Dukhovni, V., S. Huque, W. Toorop, P. Wouters, and M. Shore. TLS DNSSEC Chain Extension. RFC Editor, August 2021. http://dx.doi.org/10.17487/rfc9102.

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