[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121826-en":3,"doc-seo-121826-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121826,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction","Globally, over 17 million deaths each year stem from cardiovascular diseases, with heart disease the leading cause of mortality in the United States. The growth of heart-disease data enables machine learning (ML) for diagnosis and prediction, yet ML often requires technical expertise that can hinder healthcare professionals. Automated machine learning (AutoML) reduces this barrier by enabling effective model construction without deep coding knowledge. This study benchmarks PyCaret, AutoGluon, and AutoKeras on multiple datasets, comparing them with traditional ML pipelines built using sklearn and standard preprocessing, feature engineering, and ten baseline models.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nEvaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction  \nPermalink  \n[https://escholarship.org/uc/item/1q7563mx](https://escholarship.org/uc/item/1q7563mx)  \nJournal  \nAI, 4(4)  \nISSN  \n2673-2688  \nAuthors  \nPaladino, Lauren M  \nHughes, Alexander Perera, Alexander  \net al.  \nPublication Date  \n2023  \nDOI  \n10.3390/ai4040053  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticle  \nEvaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction  \nLauren M. Paladino 1, Alexander Hughes 1, Alexander Perera 1, Oguzhan Topsakal 1 and Tahir Cetin Akinci 2,3, *  \nCitation: Paladino, L.M.; Hughes, A.; Perera, A.; Topsakal, O.; Akinci, T.C. Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction. AI 2023, 4, 1036–1058. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/ai4040053](10.3390/ai4040053)  \nAcademic Editor: Tim Hulsen  \nReceived: 13 September 2023  \nRevised: 24 November 2023  \nAccepted: 27 November 2023  \nPublished: 1 December 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA;  \nlpaladino7605@fl[oridapoly.edu](oridapoly.edu) (L.M.P.); ahughes3300@fl[oridapoly.edu](oridapoly.edu) (A.H.);  \naperera3727@fl[oridapoly.edu](oridapoly.edu) (A.P.); otopsakal@fl[oridapoly.edu](oridapoly.edu) (O.T.)  \n2 Winston Chung Global Energy Center (WCGEC), University of California at Riverside (UCR), Riverside, CA 92521, USA  \n3 Electrical Engineering Department, Istanbul Technical University (ITU), Istanbul 34469, Turkey  \n* Correspondence: [tahircetin.akinci@ucr.edu](tahircetin.akinci@ucr.edu)  \nAbstract: Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset) . To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60% . This performance is markedly inf","cbCaircLYwIEomT0","https://ap.wps.com/l/cbCaircLYwIEomT0","pdf",1501623,1,24,"English","en",105,"# Abstract\n# Introduction\n# Materials and Methods\n## Datasets\n## AutoML Tools and Baseline Models\n# Results and Discussion\n## Performance Across Datasets\n## Comparison With Traditional ML\n# Conclusions","[{\"question\":\"Which AutoML tools are evaluated for heart disease diagnosis and prediction?\",\"answer\":\"The study evaluates three AutoML tools: PyCaret, AutoGluon, and AutoKeras, tested on heart disease datasets including Cleveland, Hungarian, and a combined dataset.\"},{\"question\":\"How does the study compare AutoML with traditional machine learning approaches?\",\"answer\":\"Traditional ML models are built using standard practices such as exploratory data analysis, data cleansing, and feature engineering, then evaluated using 5-fold cross-validation and sklearn-based pipelines.\"},{\"question\":\"What performance trend does the study report for AutoML versus conventional ML?\",\"answer\":\"Across datasets, traditional models reach about 55% to 60% accuracy, while AutoML tools achieve substantially higher performance; AutoGluon is consistently best, with accuracy roughly between 78% and 86%.\"}]","Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction | PDF",1785807062,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluating-the-performance-of-automated-machine-learning-automl-tools-for-heart-disease-diagnosis-and-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluating-the-performance-of-automated-machine-learning-automl-tools-for-heart-disease-diagnosis-and-prediction/121826/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which AutoML tools are evaluated for heart disease diagnosis and prediction?","Question",{"text":75,"@type":76},"The study evaluates three AutoML tools: PyCaret, AutoGluon, and AutoKeras, tested on heart disease datasets including Cleveland, Hungarian, and a combined dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study compare AutoML with traditional machine learning approaches?",{"text":80,"@type":76},"Traditional ML models are built using standard practices such as exploratory data analysis, data cleansing, and feature engineering, then evaluated using 5-fold cross-validation and sklearn-based pipelines.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance trend does the study report for AutoML versus conventional ML?",{"text":84,"@type":76},"Across datasets, traditional models reach about 55% to 60% accuracy, while AutoML tools achieve substantially higher performance; 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