[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119431-en":3,"doc-seo-119431-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119431,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Obesity Prediction Using Machine Learning Algorithms - Predicting Obesity Levels with ML","This study develops an obesity-level prediction model using five machine learning algorithms: K-Nearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree, Random Forest, and Support Vector Machine (SVM). The Kaggle dataset contains 2111 records with 17 lifestyle and demographic attributes. Data preprocessing and Holdout Split (70% training, 30% testing) support model training and evaluation. Performance is assessed by accuracy, precision, recall, and F1 score. Random Forest achieves the highest accuracy at 92.29%, outperforming Decision Tree and K-NN, while NBC and SVM show lower performance and difficulty separating some obesity classes.","Obesity Prediction Using Machine Learning Algorithms  \nHanifatus Syahidah1*, Novila Irsandi2,  \nAdila Nur Ajizah3 , Amelia4  \n1,2Department of Information System, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia  \n3,4Department of Business, Faculty of Economics and Administrative Sciences,  \nDicle University, Turkey  \n[E-Mail:](E-Mail:112250324302@students.uin-suska.ac.id)[1](E-Mail:112250324302@students.uin-suska.ac.id)[12250324302@students.uin-suska.ac.id](E-Mail:112250324302@students.uin-suska.ac.id), [2](212250323414@students.uin-suska.ac.id)[12250323414@students.uin-suska.ac.id](212250323414@students.uin-suska.ac.id),  \n[3](3nurajizahadila@gmail.com)[nurajizahadila@gmail.com](3nurajizahadila@gmail.com), [4](4ameliabubbletea67@gmail.com)[ameliabubbletea67@gmail.com](4ameliabubbletea67@gmail.com)  \nReceived Dec 29th 2024; Revised Feb 25th 2025; Accepted Feb 27th 2025; Available Online Feb 28th 2025, Published Feb 28th 2025  \nCorresponding Author: Hanifatus Syahidah  \nCopyright © 2025 by Authors, Published by Institut Riset dan Publikasi Indonesia (IRPI)  \nAbstract  \nThis study aims to develop a prediction model for obesity levels by utilizing five machine learning algorithms, namely KNearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree, Random Forest, and Support Vector Machine (SVM). The data used in this study were obtained from Kaggle, consisting of 2111 data with 17 attributes covering lifestyle and demographic factors. The research process involved data collection, pre-processing, data division using the Holdout Split method (70% training data and 30% testing data), and the application of machine learning algorithms. Performance evaluation used accuracy, precision, recall, and F1 score metrics. The results showed that the Random Forest algorithm had the best performance with an accuracy of 92.29%, followed by Decision Tree at 90.54%, K-NN at 83.44%, and NBC and SVM which reached 59.15% and 59.08%, respectively. Confusion matrix analysis revealed that NBC and SVM had difficulty distinguishing certain obesity classes. Based on these findings, it can be concluded that Random Forest is the most effective algorithm in predicting obesity levels. The results of this study are expected to contribute to developing amore accurate obesity prediction system that can be implemented in the real world.  \nKeyword: Decision Tree, K-NN, NBC, Random Forest, SVM, Obesity Prediction  \n1. INTRODUCTION  \nData mining is the process of exploring and analyzing large datasets to uncover patterns or extract valuable information [1][2]. One of the key techniques in data mining is classification, which aims to categorize data into specific groups based on identified patterns [3][4] . This technique is widely applied in various domains, including disease prediction, market analysis, and risk management. In this study, classification is used to analyze obesity levels based on multiple lifestyle and demographic factors. Obesity, which has become a rapidly growing public health issue [5], serves as the primary focus of this research.  \nObesity has become a global epidemic that affects all age groups and backgrounds and can occur in both adults and children [6] . In Latin America, especially in countries such as Mexico, Peru, and Colombia, the prevalence of obesity continues to increase significantly [7][8] . This alarming condition is strongly linked to various chronic diseases, including cardiovascular diseases, type 2 diabetes, hypertension, and other metabolic disorders. Identifying the factors that contribute to obesity is crucial for developing effective prevention strategies [9] . Hence, identifying the factors that lead to obesity is crucial for designing effective prevention strategies.  \nObesity and Sarcopenic Obesity (SO) are health conditions that are closely related to body composition, lifestyle, and physical activity [10] . Obesity, which is generally associated with increase","cbCainQcictkNXq0","https://ap.wps.com/l/cbCainQcictkNXq0","pdf",320437,1,10,"English","en",105,"# Abstract\n# Introduction\n## Data mining and classification for health prediction\n## Global and regional obesity context\n## Machine learning approach and algorithms","[{\"question\":\"Which algorithms are used for obesity prediction in this study?\",\"answer\":\"The study applies K-Nearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree, Random Forest, and Support Vector Machine (SVM).\"},{\"question\":\"How is the dataset prepared and split for training and testing?\",\"answer\":\"Data are preprocessed and divided using the Holdout Split method with 70% for training and 30% for testing.\"},{\"question\":\"Which algorithm performs best and what is the reported accuracy?\",\"answer\":\"Random Forest performs best with an accuracy of 92.29%, higher than Decision Tree (90.54%) and K-NN (83.44%).\"},{\"question\":\"What evaluation metrics are used to compare model performance?\",\"answer\":\"Models are evaluated using accuracy, precision, recall, and F1 score, supported by confusion matrix analysis.\"}]","Obesity Prediction Using Machine Learning Algorithms - 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