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Journal articles on the topic 'Classification and Regression tree analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Editor, IJSMI. "CART and CHAID ANALYSIS." International Journal of Statistics and Medical Informatics 15, no. 1 (2021): 1–6. https://doi.org/10.5281/zenodo.4672067.

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Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detector (CHAID) works on principles of decision tree analysis. Classification and Regression (CART) classifies the data based on the categorical outcome variable (Classification) and also uses continuous outcome variable for regression problem. Chi Square Automatic Interaction Detector (CHAID) is similar to CART which uses classifies the data into multiple class labels not only binary classification. In CHAID both dependent variable and independent variables will be categorical. This paper provides an overview and

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Wray, Charlie M., and Amy L. Byers. "Methodological Progress Note: Classification and Regression Tree Analysis." Journal of Hospital Medicine 15, no. 9 (2020): 549–51. http://dx.doi.org/10.12788/jhm.336610.12788/.

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Fan, Zhaofei, John M. Kabrick, and Stephen R. Shifley. "Classification and regression tree based survival analysis in oak-dominated forests of Missouri's Ozark highlands." Canadian Journal of Forest Research 36, no. 7 (2006): 1740–48. http://dx.doi.org/10.1139/x06-068.

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Tree survival or mortality is a stochastic process and highly variable over time and space. Many factors contribute to this process, including tree age, tree size, competition, drought, insects, and diseases. Traditional parametric approaches to modeling tree survival or mortality are often unable to capture this variation, especially in natural, mixed-species forests. We analyzed tree survival in Missouri Ozark oak forests using a combination of classification and regression tree (CART) and survival analysis of more than 35 000 trees with DBH >11 cm measured four times between 1992 and 200

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Bennett, Deborah H., Thomas E. McKone, and William E. Kastenberg. "Evaluating multimedia chemical persistence: Classification and regression tree analysis." Environmental Toxicology and Chemistry 19, no. 4 (2000): 810–19. http://dx.doi.org/10.1002/etc.5620190405.

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Sivananda, Mr M., and Dr G. Kishor Kumar. "Classification and Regression Based on Decision Tree Algorithm for Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28533.

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Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision tree

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Wolf, Jean, Randall Guensler, Simon Washington, and William Bachman. "High-Emitting Vehicle Characterization Using Regression Tree Analysis." Transportation Research Record: Journal of the Transportation Research Board 1641, no. 1 (1998): 58–65. http://dx.doi.org/10.3141/1641-07.

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A small fraction of motor vehicles on the roadway emit a disproportionate fraction of pollutant emissions, especially for carbon monoxide and hydrocarbons. Generally, these “high emitters” or “super emitters” exhibit higher emissions rates under all operating conditions than do “normal emitters.” Since the instantaneous emissions response between normal- and high-emitting vehicles can differ by one or more orders of magnitude, so do their average emissions over a “typical” trip. Identifying the proportion of normal- and high-emitting vehicles in an urban area and quantifying their emissions is

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Tung, Heng-Hsin, Chiang-Yi Chen, Kuan-Chia Lin, et al. "Classification and regression tree analysis in acute coronary syndrome patients." World Journal of Cardiovascular Diseases 02, no. 03 (2012): 177–83. http://dx.doi.org/10.4236/wjcd.2012.23030.

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Suwardika, Gede, I. Ketut Putu Suniantara, and Ni Putu Nanik Hendayanti. "Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart." Jurnal VARIAN 2, no. 2 (2019): 37–46. http://dx.doi.org/10.30812/varian.v2i2.361.

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The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one

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Bello, Safinatu, Ahmad Abubakar Aliyu, Muhammad Aminu Ahmad, et al. "AN ENHANCED CLASSIFICATION AND REGRESSION TREE ALGORITHM USING GINI EXPONENTIAL." FUDMA JOURNAL OF SCIENCES 9, no. 3 (2025): 259–67. https://doi.org/10.33003/fjs-2025-0903-3321.

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Decision tree algorithms, particularly Classification and Regression Trees (CART), are widely used in machine learning for their simplicity, interpretability, and ability to handle both categorical and numerical data. However, traditional decision trees often encounter limitations when dealing with complex, high-dimensional, or imbalanced datasets, as conventional impurity measures such as the Gini Index and Information Gain may fail to capture subtle variations in the data effectively. This study enhances the traditional Classification and Regression Trees (CART) model by introducing the Gini

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Okada, Hugo Kenji Rodrigues, Andre Ricardo Nascimento das Neves, and Ricardo Shitsuka. "Analysis of Decision Tree Induction Algorithms." Research, Society and Development 8, no. 11 (2019): e298111473. http://dx.doi.org/10.33448/rsd-v8i11.1473.

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Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was consider

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Fernanda, J. W., G. Anuraga, and M. A. Fahmi. "Risk factor analysis of hypertension with logistic regression and Classification and Regression Tree (CART)." Journal of Physics: Conference Series 1217 (May 2019): 012109. http://dx.doi.org/10.1088/1742-6596/1217/1/012109.

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Nasution, Budiman, Winsyahputra Ritonga, Ruben Cornelius Siagian, et al. "Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression." Jurnal Penelitian Pendidikan IPA 9, no. 11 (2023): 9532–46. http://dx.doi.org/10.29303/jppipa.v9i11.4483.

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This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was no

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Bevilacqua, Maurizio, Marcello Braglia, and Roberto Montanari. "The classification and regression tree approach to pump failure rate analysis." Reliability Engineering & System Safety 79, no. 1 (2003): 59–67. http://dx.doi.org/10.1016/s0951-8320(02)00180-1.

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Hong, Wandong, Lemei Dong, Qingke Huang, Wenzhi Wu, Jiansheng Wu, and Yumin Wang. "Prediction of Severe Acute Pancreatitis Using Classification and Regression Tree Analysis." Digestive Diseases and Sciences 56, no. 12 (2011): 3664–71. http://dx.doi.org/10.1007/s10620-011-1849-x.

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Garzotto, Mark, Tomasz M. Beer, R. Guy Hudson, et al. "Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis." Journal of Clinical Oncology 23, no. 19 (2005): 4322–29. http://dx.doi.org/10.1200/jco.2005.11.136.

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Purpose To build a decision tree for patients suspected of having prostate cancer using classification and regression tree (CART) analysis. Patients and Methods Data were uniformly collected on 1,433 referred men with a serum prostate-specific antigen (PSA) levels of ≤ 10 ng/mL who underwent a prostate biopsy. Factors analyzed included demographic, laboratory, and ultrasound data (ie, hypoechoic lesions and PSA density [PSAD]). Twenty percent of the data was randomly selected and reserved for study validation. CART analysis was performed in two steps, initially using PSA and digital rectal exa

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Kim, Tae-Woo, Dong-Hee Koh, and Chung-Yill Park. "Decision Tree of Occupational Lung Cancer Using Classification and Regression Analysis." Safety and Health at Work 1, no. 2 (2010): 140–48. http://dx.doi.org/10.5491/shaw.2010.1.2.140.

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Ross, Emma H., and Christopher A. Kearney. "Posttraumatic symptoms among maltreated youth using classification and regression tree analysis." Child Abuse & Neglect 69 (July 2017): 177–87. http://dx.doi.org/10.1016/j.chiabu.2017.04.028.

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Subbarao Manne, Venkata. "Heart Failure Predictive Analysis Using Decision Tree Classification." International Journal of Science and Healthcare Research 9, no. 2 (2024): 289–96. http://dx.doi.org/10.52403/ijshr.20240239.

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With an average age of 28 compared to Western countries, India's young population accounts for half of heart attacks in South Asia, which happen to people under 52. Autopsy reports, which identify the actual cause of death, frequently concentrate on sudden deaths in young adults that have no apparent reason or warning signs. Fat accumulation in the blood vessels of the heart is the cause of abrupt, unexpected natural deaths. The heart stops beating and loses blood as a result of these arteries narrowing or blocking. The body may exhibit subtle symptoms prior to abrupt death, such as shortness

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Aguilar-Reyes, Johanna Enith, Edwin Fernando Mejía-Peñafiel, Tania Paulina Morocho-Barrionuevo, and Geoconda-Marisela Velasco Castelo. "Estudio del rendimiento académico mediante la comparación de modelos de regresión y árboles de clasificación." Telos: Revista de Estudios Interdisciplinarios en Ciencias Sociales 27, no. 1 (2025): 94–115. https://doi.org/10.36390/telos271.08.

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This article aims to identify the factors that affect academic performance by comparing regression models and decision trees to determine the factors involved. The methodology adopted is quantitative in nature, focused on the collection of numerical data and its statistical analysis, in order to evaluate the relationships between different variables and determine those factors that influence academic performance. The population studied includes remedial students in the statistics career, who underwent an exploratory and descriptive analysis, using two statistical methods. Two modeling techniqu

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Jajang, Jajang, Nunung Nurhayati, and Suci Jena Mufida. "ORDINAL LOGISTIC REGRESSION MODEL AND CLASSIFICATION TREE ON ORDINAL RESPONSE DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (2022): 075–82. http://dx.doi.org/10.30598/barekengvol16iss1pp075-082.

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Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables. The prediction of the LR model is in the form of probability. This research studied logistic regression (LR) models and Classification Trees in the case of ordinal response variable types. The data used in this research from The Central Statistics Agency (BPS). The research variables used are Human Development Index (HDI), gross enrollment rate for high school, percentage of poor people, open unemployment, and perce

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Aguspri, Amellia. "Analisis Financial Distress pada Bank Syariah di Indonesia Menggunakan Metode Classification and Regression Tree (CART)." Jurnal Ilmiah Ekonomi Islam 10, no. 1 (2024): 292. http://dx.doi.org/10.29040/jiei.v10i1.11148.

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Using the Grover and Classification and Regression Tree (CART) techniques, this research aims to evaluate the factors that influence the financial conditions of Islamic banks from 2010 to 2021. The sample for this research was taken using a purposive sampling technique from five banks registered in OJK from 2010 to 2021. The results of this research using Grover analysis show that no bank has experienced bankruptcy with an accuracy value of 100%. On the other hand, according to decision tree regression using the Classification and Regression Trees (CART) method, FDR, NPM and CAR are the three

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Meftakhetdinova, J., and Z. Maharramov. "DECISION TREE ANALYSIS AND LOGISTIC REGRESSION ANALYSIS AS ANALYTICAL METHODS OF A DECISION SUPPORT SYSTEM." Sciences of Europe, no. 112 (March 8, 2023): 69–73. https://doi.org/10.5281/zenodo.7708542.

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The purpose of the study is to compare the most appropriate classification algorithms for making a decision, such as yes or no, 0 or 1, sick or not sick, qualifying for benefits or not, and so on. The article discusses two classification algorithms, decision tree analysis and logistic regression analysis. The focus is on the suitability of the algorithm for the chosen field of study. As a result, it was determined that logistic regression analysis is more suitable for making decisions of this type.

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Barlin, Joyce N., Qin Zhou, Caryn M. St. Clair, et al. "Classification and regression tree (CART) analysis of endometrial carcinoma: Seeing the forest for the trees." Gynecologic Oncology 130, no. 3 (2013): 452–56. http://dx.doi.org/10.1016/j.ygyno.2013.06.009.

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Ismail, Rasha Rokan, and Farah Hatem Khorsheed. "Classification of Credit Card Frauds Detection using machine learning techniques." JEECOM Journal of Electrical Engineering and Computer 5, no. 2 (2023): 153–60. http://dx.doi.org/10.33650/jeecom.v5i2.6602.

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Credit card fraud refers to the illegal activities carried out by criminals. In this research paper, we delve into the topic by exploring four different approaches to analyze fraud, namely decision trees, logistic regression, support vector machines, and Random Forests. Our proposed technique encompasses four stages: inputting the dataset, balancing the data through sampling, training classifier models, and detecting fraud. To analyze the data, we utilized two methods: forward stepwise logistic regression analysis (LR) and decision tree analysis (DT), in addition to Random Forest and support v

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Suwardika, Gede Suwardika, and I. Ketut Putu Suniantara. "ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 13, no. 3 (2019): 177–84. http://dx.doi.org/10.30598/barekengvol13iss3pp177-184ar910.

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Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classifica

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Lemon, Stephenie C., Jason Roy, Melissa A. Clark, Peter D. Friedmann, and William Rakowski. "Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression." Annals of Behavioral Medicine 26, no. 3 (2003): 172–81. http://dx.doi.org/10.1207/s15324796abm2603_02.

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Afolabi-Yusuf, Ganiyat Kemi, Y. O. Olatunde, K. Y. Obiwusi, M. O. Yusuf, and O. C. Abikoye. "PERFORMANCE ANALYSIS OF SELECTED CLASSIFICATION ALGORITHMS ON ANDROID MALWARE DETECTION." International Journal of Software Engineering and Computer Systems 9, no. 2 (2024): 140–49. http://dx.doi.org/10.15282/ijsecs.9.2.2023.7.0118.

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Android mobile devices are widely used across all platforms and the development of malicious apps can compromise a user’s mobile system. Considering the large amount of new malicious apps, there is a need for a detection system that can operate efficiently to identify these apps. The study analyzes and compares the performance of DREBIN and MALGENOME data sets with the dataset’s SMOTE version on selected machine learning algorithms using WEKA tools. The performance of bayesian, function, rule, and tree-based classification algorithms on the two datasets was explored in this work. WEKA tool was

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Lestari. YR, Novia Yustika Tri, Utami Dyah Syafitri, and Mulianto Raharjo. "Faktor-Faktor yang Memengaruhi Keberhasilan Studi Mahasiswa IPB Jalur Ketua OSIS dengan Metode Pohon Regresi." Xplore: Journal of Statistics 11, no. 2 (2022): 70–83. http://dx.doi.org/10.29244/xplore.v11i2.863.

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The success of IPB student's study can be seen from the achievement index obtained at the end of each semester. Meanwhile, the success rate of one's study is generally influenced by two factors, internal factors and external factors. Internal factors consist of intelligence (intellectual), physical, attitudes, interests, talents, and motivations, while external factors consist of family circumstances, school conditions, and the community environment. Therefore, this study uses the analysis method of classification and regression trees (CART) to find out what factors influenced the success of t

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Airlangga, Gregorius. "Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 1 (2024): 54–62. https://doi.org/10.12928/biste.v6i1.10059.

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Forest ecosystems play a pivotal role in maintaining global biodiversity and climate balance. The precise identification of tree species via remote sensing technologies is vital for effective ecological surveillance and forest stewardship. This research conducts a comparative analysis of various machine learning algorithms for the binary classification of tree species utilizing LiDAR data captured by Unmanned Aerial Vehicles (UAVs). We analyzed a dataset featuring 192 trees from a diverse forest, employing models such as Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Neare

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Kim, H., and R. Dionne. "Classification and Regression Tree (CART) analysis of experimental pain sensitivity in humans." Journal of Pain 6, no. 3 (2005): S5. http://dx.doi.org/10.1016/j.jpain.2005.01.020.

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Lama, Yuki, Gregory R. Hancock, Vicki S. Freimuth, Amelia M. Jamison, and Sandra Crouse Quinn. "Using classification and regression tree analysis to explore parental influenza vaccine decisions." Vaccine 38, no. 5 (2020): 1032–39. http://dx.doi.org/10.1016/j.vaccine.2019.11.039.

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Kominiarek, Michelle, Paul VanVeldhuisen, Kimberly Gregory, Moshe Fridman, and Judith Hibbard. "679: Predicting delivery route in nulliparas with classification and regression tree analysis." American Journal of Obstetrics and Gynecology 204, no. 1 (2011): S268. http://dx.doi.org/10.1016/j.ajog.2010.10.699.

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Arifuzzaman, Md, Uneb Gazder, Md Shah Alam, Okan Sirin, and Abdullah Al Mamun. "Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis." Computational Intelligence and Neuroscience 2019 (August 15, 2019): 1–7. http://dx.doi.org/10.1155/2019/3183050.

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The modification by polymers and nanomaterials can significantly improve different properties of asphalt. However, during the service life, the oxidation affects the constituents of modified asphalt and subsequently results in deviation from the desired properties. One of the important properties affected due to oxidation is the adhesive properties of modified asphalt. In this study, the adhesive properties of asphalt modified with the polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated. Asphalt samples were aged in the laboratory by simulating the

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Stamilio, David, Alison Cahill, Anthony Odibo, William Shannon, Jeffrey Peipert, and George Macones. "660: Uterine rupture in VBAC: Prediction with classification & regression tree analysis." American Journal of Obstetrics and Gynecology 197, no. 6 (2007): S190. http://dx.doi.org/10.1016/j.ajog.2007.10.687.

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Xu, Xuecai, Željko Šarić, and Ahmad Kouhpanejade. "Freeway Incident Frequency Analysis Based on CART Method." PROMET - Traffic&Transportation 26, no. 3 (2014): 191–99. http://dx.doi.org/10.7307/ptt.v26i3.1308.

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Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods.

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A.M, Najla Ilyana, Nor Aini Z, Sharvin R., and Norlaili M.S. "Analysis of surface electromyography for hand gesure classification." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1366. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1366-1373.

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<span lang="EN-MY">Electromyography (EMG) is the measure of electrical activity produced by skeletal muscle. It is useful in prosthetic and rehabilitation technology as well as ability to handle electronic devices and robotics. If the EMG signal from the body especially hand movement can be apprehended, better value for people all around the world can be provided. Furthermore, it can be used to control smart-phone and be integrated with wearable technology. Another interesting application of this technology is in sign language recognition which is able to assist many disabled people in t

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Zhu, Ru, Hua Duan, Sha Wang, Lu Gan, Qian Xu, and Jinjiao Li. "Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine." BioMed Research International 2019 (December 12, 2019): 1–8. http://dx.doi.org/10.1155/2019/7391965.

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Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results. Patients’ demographics, clinical indicators

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Uddameri, Venkatesh, Ana Silva, Sreeram Singaraju, Ghazal Mohammadi, and E. Hernandez. "Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas." Water 12, no. 4 (2020): 1023. http://dx.doi.org/10.3390/w12041023.

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The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predictin

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Quantin, C., L. Billard, M. Touati, et al. "Classification and Regression Trees on Aggregate Data Modeling: An Application in Acute Myocardial Infarction." Journal of Probability and Statistics 2011 (2011): 1–19. http://dx.doi.org/10.1155/2011/523937.

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Cardiologists are interested in determining whether the type of hospital pathway followed by a patient is predictive of survival. The study objective was to determine whether accounting for hospital pathways in the selection of prognostic factors of one-year survival after acute myocardial infarction (AMI) provided a more informative analysis than that obtained by the use of a standard regression tree analysis (CART method). Information on AMI was collected for 1095 hospitalized patients over an 18-month period. The construction of pathways followed by patients produced symbolic-valued observa

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Hobcraft, John, and Wendy Sigle-Rushton. "Identifying Patterns of Resilience Using Classification Trees." Social Policy and Society 8, no. 1 (2009): 87–98. http://dx.doi.org/10.1017/s1474746408004612.

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This paper introduces and illustrates the value of a classification trees approach in the study of resilience. The inherently interactive nature of the resilience construct makes this approach useful. Classification trees are a person-centred approach to data analysis, which successively split the sample into pairs of increasingly homogeneous groups of individuals. We outline the approach and then illustrate using adult educational outcomes for children in the British Cohort Study of 1970 who had experienced foster care. The insights gained from the classification tree approach are contrasted

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Mardiani, Eri, Nur Rahmansyah, Andy Setiawan, Zakila Cahya Ronika, Dini Fatihatul Hidayah, and Atira Syakira. "COMPARISON OF KNN, NAIVE BAYES, DECISION TREE, ENSEMBLE, REGRESSION METHODS FOR INCOME PREDICTION." Jurnal Techno Nusa Mandiri 20, no. 2 (2023): 115–21. http://dx.doi.org/10.33480/techno.v20i2.4613.

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Using the income classification dataset, we performed data analysis with the help of data mining to gather interesting information from the available data. Currently, data processing can be done using many tools. One of the tools that we use for data processing is the orange application. By using the dataset we looked at the welfare level ranging from marital status, school, gender, and from all fields related to income ranging from sales, to daily life to find out the income earned by employees or workers from several countries such as the United States, Cambodia, United Kingdom, Puerto-Rico,

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Fan, Zhaofei, Stephen R. Shifley, Martin A. Spetich, Frank R. Thompson III, and David R. Larsen. "Distribution of cavity trees in midwestern old-growth and second-growth forests." Canadian Journal of Forest Research 33, no. 8 (2003): 1481–94. http://dx.doi.org/10.1139/x03-068.

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We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in old-growth hardwood forests in Missouri, Illinois, and Indiana found that 811% of snags had at least one visible cavity (as visually detected from the ground; smallest opening [Formula: see text]2 cm diameter), about twice the

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Bae, Hun-Kyun, Betty H. Olson, Kuo-Lin Hsu, and Soroosh Sorooshian. "Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area." Water Science and Technology 61, no. 2 (2010): 545–53. http://dx.doi.org/10.2166/wst.2010.842.

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The study used existing indicator bacterial data and a number of physicochemical parameters that can be measured instantaneously to determine if a decision tree approach, especially classification and regression tree, could be used to predict bacterial concentrations in timely manner for beach closure management. Each indicator bacteria showed different tree structures and each had its own significant variables; Dissolved oxygen played an important role for both total coliform and fecal coliform and turbidity was the most important factor to predict concentrations of enterococci for decision t

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Nur Fadillah, Nur, Syafriandi Syafriandi, Nonong Amalita, and Dony Permana. "Classification of Unemployment at West Sumatra Province in 2021 using Algorithm Classification and Regression Tree." UNP Journal of Statistics and Data Science 2, no. 2 (2024): 179–86. http://dx.doi.org/10.24036/ujsds/vol2-iss2/166.

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The problem of unemployment is a problem that often occurs in developing countries. This is caused by an imbalance between the number of the workforce and the number of working people. According to the Central Statistics Agency, West Sumatra Province in 2021 is the eighth province that has a high open unemployment rate namely 6,52%, which is higher than the average open unemployment rate in Indonesia namely 6,49%. An increase in unemployment has occurred from 2017 to 2021 which is caused by educated unemployment. This is caused by the habits of job seekers who tend to choose existing types of

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Babar, Kiran Nitin. "Performance Evaluation of Decision Trees with Machine Learning Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34179.

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Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition and Data Mining have dealt with the issue of growing a decision tree from available data. Decision trees in machine learning will be used for classification problems, to categorize objects to gain an understanding of similar features. Decision trees helps in decision-making by representing co

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Lo, BenjaminW Y., Hitoshi Fukuda, Mark Angle, et al. "Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis." Surgical Neurology International 7, no. 1 (2016): 73. http://dx.doi.org/10.4103/2152-7806.185786.

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Achenbach, Peter, Markus Hippich, Jose Zapardiel-Gonzalo, et al. "A classification and regression tree analysis identifies subgroups of childhood type 1 diabetes." eBioMedicine 82 (August 2022): 104118. http://dx.doi.org/10.1016/j.ebiom.2022.104118.

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Ye, Xuhui, Yuxuan Tang, Daode Zhang, and Xinyu Hu. "PCB PAD DETECTION ALGORITHM BASED ON PRINCIPAL COMPONENT ANALYSIS AND CLASSIFICATION REGRESSION TREE." Journal of Flow Visualization and Image Processing 29, no. 1 (2022): 89–107. http://dx.doi.org/10.1615/jflowvisimageproc.2021039569.

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Schmidt, G. W., A. T. Broman, H. B. Hindman, and Michael P. Grant. "Vision Survival after Open Globe Injury Predicted by Classification and Regression Tree Analysis." Ophthalmology 115, no. 1 (2008): 202–9. http://dx.doi.org/10.1016/j.ophtha.2007.04.008.

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Emrouznejad, Ali, and Abdel Latef Anouze. "Data envelopment analysis with classification and regression tree - a case of banking efficiency." Expert Systems 27, no. 4 (2010): 231–46. http://dx.doi.org/10.1111/j.1468-0394.2010.00516.x.

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