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Journal articles on the topic 'AdaBoost CN2 rule inducer'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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1

Arabiat, Areen, and Muneera Altayeb. "Driving behavior analytics: an intelligent system based on machine learning and data mining techniques." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2055–65. https://doi.org/10.11591/eei.v14i3.9095.

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One of the most common causes of road accidents is driver behavior. To reduce abnormal driver behavior, it must be detected early on. Previous research has demonstrated that behavioral and physiological indicators affect drivers' performance. The goal of this study is to consider the feasibility of classifying driver behavior as either aggressive (sudden left or right turns, accelerating and braking), normal (average driving events) or slow (keeping a lower-than-average speed). Innovation in data mining and machine learning (ML) has allowed for the creation of powerful prediction tools. ML tec
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2

Salem Alzboon, Mowafaq, Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Ahmad Abuashour, and Ahmad Fuad Hamadah Bader. "Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 15 (2023): 144–65. http://dx.doi.org/10.3991/ijoe.v19i15.42417.

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Detection and management of diabetes at an early stage is essential since it is rapidly becoming a global health crisis in many countries. Predictions of diabetes using machine learning algorithms have been promising. In this work, we use data collected from the Pima Indians to assess the performance of multiple machine-learning approaches to diabetes prediction. Ages, body mass indexes, and glucose levels for 768 patients are included in the data set. The methods evaluated are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient
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3

Eid, Waad Mohammed, Hana Alharthi, Nida Aslam, Irfan Ullah Abdur rab, and Alaa Madani. "Predicting diabetic ketoacidosis in pediatric patients using machine learning." F1000Research 12 (June 6, 2023): 611. http://dx.doi.org/10.12688/f1000research.130042.1.

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Background Machine learning is a powerful tool to define relationships between large data variables through computing algorithms. In medicine, machine learning can find the association between a given disease and disease-related complications such as the relationship between Diabetes and development of diabetic ketoacidosis (DKA). The aim of this study is to develop and evaluate a predicting model for diabetic ketoacidosis among pediatric cases to define the leading factors that can predict diabetic ketoacidosis. Methods We evaluated the medical records of 3737 pediatric patients between the a
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4

Al-batah, Mohammad, Mohammad Al-Batah, Mowafaq Salem Alzboon, and Esra Alzaghoul. "Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision." Data and Metadata 4 (January 1, 2025): 460. http://dx.doi.org/10.56294/dm2025460.

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This article uses machine learning to quantify vesicoureteral reflux (VUR). VCUGs in pediatric urology are used to diagnose VUR. The goal is to increase diagnostic precision. Various machine learning models categorize VUR grades (Grade 1 to Grade 5) and are evaluated using performance metrics and confusion matrices. Study datasets come from internet repositories with repository names and accession numbers. Machine learning models performed well across several measures. KNN, Random Forest, AdaBoost, and CN2 Rule Induction consistently scored 100% in AUC, CA, F1-score, precision, recall, MCC, an
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5

Zahid, Muhammad, Yangzhou Chen, Arshad Jamal, Khalaf A. Al-Ofi, and Hassan M. Al-Ahmadi. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study." International Journal of Environmental Research and Public Health 17, no. 14 (2020): 5193. http://dx.doi.org/10.3390/ijerph17145193.

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Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained fro
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6

Oktanisa, Irvi, and Ahmad Afif Supianto. "Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing." Jurnal Teknologi Informasi dan Ilmu Komputer 5, no. 5 (2018): 567. http://dx.doi.org/10.25126/jtiik.201855958.

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<p class="Abstrak">Klasifikasi merupakan teknik dalam <em>data mining</em> untuk mengelompokkan data berdasarkan keterikatan data terhadap data sampel. Pada penelitian ini, kami melakukan perbandingan 9 teknik klasifikasi untuk mengklasifikasi respon pelanggan pada <em>dataset Bank Direct Marketing</em>. Perbandingan teknik klasifikasi ini dilakukan untuk mengetahui model dalam teknik klasfikasi yang paling efektif untuk mengklasifikasi target pada <em>dataset Bank Direct Marketing</em>. Teknik klasifikasi yang digunakan yaitu <em>Support Vector
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7

Alka, Mishra, Jacob Mathew Abhilash, Agrawal Abhyudaya, Quarishi Adnan, Vaishnava Amiy, and Kumar Sparsh. "Sensor based sign language recognition system." i-manager’s Journal on Pattern Recognition 9, no. 1 (2022): 15. http://dx.doi.org/10.26634/jpr.9.1.18757.

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Sign language is used as a primary form of communication by many people who are deaf, deafened and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have enabled development of innovative approaches to translate these sign languages to spoken languages. This paper proposes an intelligent system for translating sign language into text. This approach consists of hardware as well as software. The hardware consists of flex, conta
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Hasan, Raza, Sellappan Palaniappan, Salman Mahmood, Ali Abbas, Kamal Uddin Sarker, and Mian Usman Sattar. "Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques." Applied Sciences 10, no. 11 (2020): 3894. http://dx.doi.org/10.3390/app10113894.

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Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using
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9

R, Sujatha, Aarthy SL, Jyotir Moy Chatterjee, A. Alaboudi, and NZ Jhanjhi. "A Machine Learning Way to Classify Autism Spectrum Disorder." International Journal of Emerging Technologies in Learning (iJET) 16, no. 06 (2021): 182. http://dx.doi.org/10.3991/ijet.v16i06.19559.

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In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various
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10

Areen, Arabiat, and Altayeb Muneera. "Driving behavior analytics: an intelligent system based on machine learning and data mining techniques." May 16, 2025. https://doi.org/10.11591/eei.v14i3.9095.

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One of the most common causes of road accidents is driver behavior. To reduce abnormal driver behavior, it must be detected early on. Previous research has demonstrated that behavioral and physiological indicators affect drivers' performance. The goal of this study is to consider the feasibility of classifying driver behavior as either aggressive (sudden left or right turns, accelerating and braking), normal (average driving events) or slow (keeping a lower-than-average speed). Innovation in data mining and machine learning (ML) has allowed for the creation of powerful prediction tools. ML tec
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11

Shin, Dayeon, and Junguk Hur. "Predictive Modeling of Postpartum Depression Using Machine Learning Approaches (P18-130-19)." Current Developments in Nutrition 3, Supplement_1 (2019). http://dx.doi.org/10.1093/cdn/nzz039.p18-130-19.

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Abstract Objectives Postpartum depression is a serious health issue beyond mental problem that affects mothers after childbirth. There are no predictive tools available to screen postpartum depression that allow early interventions. We aimed to develop predictive models for postpartum depression using maternal and paternal characteristics based on Machine Learning (ML) approaches. Methods We performed a retrospective cohort study using data from the Pregnancy Risk Assessment Monitoring System (PRAMS), 2012–2013 (n = 72,541). Significant maternal and paternal risk factors in relation to postpar
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12

Tabatabaei Nodoushan1, Seyed MohammadReza, Fatemeh Saadatjoo, and Masoud Mirzaei. "The prediction model for cardiovascular disease using Yazd's health study data (YaHS)." Journal of Shahid Sadoughi University of Medical Sciences, July 2, 2019. http://dx.doi.org/10.18502/ssu.v27i3.1188.

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Introdution: Ischemic heart disease is one of the most common diseases, which has led to high mortality rates all over the world. This disease is caused by narrowing or blockage of coronary arteries, which are the provider of blood to the heart. Identifying the people susceptible to this disease and bringing changes in their lifestyles has been said to reduce the related mortality rates and increase the patient's longevity.
 Methods: Yazd people Health Study (YaHS) was conducted on a random sample of 10,000 people living in the city of Yazd, Iran in the years 2014-15 for a general health
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13

Asaduzzaman, Sayed, Md Raihan Ahmed, Hasin Rehana, Setu Chakraborty, Md Shariful Islam, and Touhid Bhuiyan. "Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective." BMC Bioinformatics 22, no. 1 (2021). http://dx.doi.org/10.1186/s12859-021-04131-6.

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Abstract Background In this research, an astute system has been developed by using machine learning and data mining approach to predict the risk level of cervical and ovarian cancer in association to stress. Results For functioning factors and subfactors, several machine learning models like Logistics Regression, Random Forest, AdaBoost, Naïve Bayes, Neural Network, kNN, CN2 rule Inducer, Decision Tree, Quadratic Classifier were compared with standard metrics e.g., F1, AUC, CA. For certainty info gain, gain ratio, gini index were revealed for both cervical and ovarian cancer. Attributes were r
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14

H M, Mallikarjun, and P. Manimegalai. "Manoglanistara - Emotional Wellness Phases Prediction of Adolescent Female Students by using Brain waves." Current Signal Transduction Therapy 14 (July 3, 2019). http://dx.doi.org/10.2174/1574362414666190703151853.

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: Depression is the most underestimated and widespread health condition among people in developing countries. Depression levels among Indian population are rapidly increasing. It can be attributed to work pressure, social challenges, addiction to social media, adoption of the western culture and several other reasons. Indians’ depression levels are as high as 36 per cent and shockingly this number is the highest in the world. What makes this even more alarming is the fact that WHO projects depression to be the second leading cause of disability worldwide by 2020. In this work, the focus is on
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15

YILMAZ, Burak, Güzin ÖZMEN, and Hakan EKMEKCİ. "PREDICTION OF CERVICAL DISC HERNIATION DISEASE UTILIZING TRAPEZIUS sEMG SIGNALS WITH MACHINE LEARNING TECHNIQUES BASED ON FREQUENCY DOMAIN FEATURE EXTRACTION." Konya Journal of Engineering Sciences, March 1, 2023, 205–19. http://dx.doi.org/10.36306/konjes.1185629.

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Cervical disk herniation (CDH) is a disease that affects the quality of life of many people due to the neck pain it causes. The aim of this study was to develop an automatic prediction system to aid in diagnosis by evaluating the change in the surface electrical activity of the trapezius muscle in SDH disease in order to find an answer to the question: 'Can the surface electromyogram (sEMG) recorded from the trapezius muscle be an effective indicator for the diagnosis of SDH disease?'. To this end, a dataset will be created using preprocessing and feature extraction methods from sEMG signals f
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16

Teitcher, Michael, Konrad Kubicki, Pravesh Saini, et al. "Abstract MP32: Machine Learning Models Identify Predictors of Poor Outcome in Patients Undergoing Mechanical Thrombectomy for Acute Ischemic Stroke." Stroke 52, Suppl_1 (2021). http://dx.doi.org/10.1161/str.52.suppl_1.mp32.

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Introduction: Predicting outcome after mechanical thrombectomy (MT) for ischemic stroke due to LVO can inform prognosis and guide early management. Prior studies report heterogeneity in risk factors for poor outcome. Machine learning may identify patterns of poor outcome from diverse variables that are difficult to discern with conventional statistical methods. Methods: Using a retrospective database of 233 stroke patients (2015-20) who had MT for LVO, we created machine learning predictive models with clinical and imaging variables for the following 4 outcomes: decompressive craniectomy, disc
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