[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120515-en":3,"doc-seo-120515-105":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":20,"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},120515,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Predicting Heart Disease Using Machine Learning Models - Research Project","Heart disease remains the leading cause of death in the United States, especially among older adults. This study applies machine learning to a BRFSS 2020 subset, filtering respondents aged 70 and above, to build predictive models. Logistic regression, random forests, and XGBoost are trained and evaluated using sensitivity, specificity, accuracy, and ROC AUC. Results indicate logistic regression as a strong interpretable baseline while ensemble methods provide improved predictive performance for complex, high-dimensional patterns. The project concludes by discussing public health screening implications.","The University of Akron  \nIdeaExchange@UAkron  \n\n| Williams Honors College, Honors Research Projects | The Dr. Gary B. and Pamela S. Williams Honors College |\n| --- | --- |\n| Spring 2025\u003Cbr>Predicting Heart Disease Using Machine Learning Models\u003Cbr>Zeynep Cetin[zc39@uakron.edu](zc39@uakron.edu)\u003Cbr>Follow this and additional works at: [https://ideaexchange.uakron.edu/honors_research_projects](https://ideaexchange.uakron.edu/honors_research_projects)\u003Cbr> Part of the Data Science Commons\u003Cbr>Please take a moment to share how this work helps you through this survey. Your feedback will be important as we plan further development of our repository. |  |\n\nRecommended Citation  \nCetin, Zeynep, \"Predicting Heart Disease Using Machine Learning Models\" (2025) . Williams Honors College, Honors Research Projects. 2005.  \n[https://ideaexchange.uakron.edu/honors_research_projects/2005](https://ideaexchange.uakron.edu/honors_research_projects/2005)  \nThis Dissertation/Thesis is brought to you for free and open access by The Dr. Gary B. and Pamela S. Williams Honors College at IdeaExchange@UAkron, the institutional repository of The University of Akron in Akron, Ohio, USA. It has been accepted for inclusion in Williams Honors College, Honors Research Projects by an authorized administrator of IdeaExchange@UAkron. For more information, [please contact mjon@uakron.edu](please contact mjon@uakron.edu), [uapress@uakron.edu](uapress@uakron.edu).  \nPredicting Heart Disease Using Machine Learning Models  \nZeynep Cetin  \nDepartment of Statistics, The University of Akron  \nSTAT 498: Senior Honors Project  \nApril 2025  \nTable of Contents  \nTable of Contents………………………………………………………………………………….2  \nAbstract……………………………………………………………………………………………3  \nIntroduction………………………………………………………………………………………..4  \nData Preparation…………………………………………………………………………………...5  \nDescriptive Statistics………………………………………………………………………………7  \nLogistic Regression………………………………………………………………………………16  \nRandom Forest…………………………………………………………………………………...21  \nXGBoost…………………………………………………………………………………………24  \nResults Comparison…………………...…………………………………………………………28  \nDiscussion and Future Work……………………………………………………………………..30  \nWorks Cited…………………………………………………………………………………...…31  \nAppendix: Code in R…………………………………………………………………………….33  \nAbstract  \nHeart disease remains the leading cause of death in the United States, particularly among the elderly population. The growing availability of large-scale health data and the advancement of machine learning tools present an opportunity to create more accurate and individualized predictive models. This study utilizes a subset of the 2020 Behavioral Risk Factor Surveillance System (BRFSS) dataset, focusing on individuals aged 70 and above, to explore predictive modeling using logistic regression, random forests, and XGBoost. The models were evaluated using key performance metrics, including sensitivity, specificity, accuracy, and the area under the ROC curve (AUC) . The findings suggest that while logistic regression remains a strong baseline due to its interpretability, ensemble learning methods like the random forest and XGBoost show superior predictive performance, particularly in complex, high-dimensional settings. The paper is concluded by discussing the potential of integrating such models into public health screening and the broader implications.  \nIntroduction  \nHeart disease, encompassing conditions such as coronary artery disease, heart failure, and arrhythmia, continues to exert an enormous burden on global health. In the U.S., heart disease is the leading cause of death is heart disease for individuals aged 75 and older (Stanford Geriatrics) . As healthcare systems increasingly shift toward proactive care and prevention, early identification of individuals at risk becomes a pivotal strategy.  \nTraditional clinical risk scores, while valuable, may not fully capture the complex interplay between genetic, behavioral, and environmental risk factors. Machine learning techniques, with t","cbCaifsWQkE59fGm","https://ap.wps.com/l/cbCaifsWQkE59fGm","pdf",1139924,1,45,"English","en",105,"# Abstract\n# Introduction\n# Data Preparation\n## Variable Selection and Cleaning\n## Descriptive Statistics\n# Logistic Regression\n# Random Forest\n# XGBoost\n# Results Comparison\n# Discussion and Future Work\n# Works Cited\n# Appendix: Code in R","[{\"question\":\"What dataset and population are used to predict heart disease risk?\",\"answer\":\"The study uses a subset of the BRFSS 2020 dataset, filtered to include respondents aged 70 and above.\"},{\"question\":\"Which machine learning models are compared in this project?\",\"answer\":\"Logistic regression, random forests, and XGBoost are evaluated for predictive performance.\"},{\"question\":\"How are the models evaluated?\",\"answer\":\"Models are assessed using sensitivity, specificity, accuracy, and the area under the ROC curve (AUC).\"},{\"question\":\"What is the key takeaway from the comparison results?\",\"answer\":\"Logistic regression provides a strong interpretable baseline, while ensemble methods like random forest and XGBoost show superior performance, particularly for complex patterns.\"}]","Predicting Heart Disease Using Machine Learning Models - 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