[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125499-en":3,"doc-seo-125499-105":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},125499,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","A Comparative Study of Machine Learning and Deep Learning Models for Heart Disease Classification","Heart disease remains a major cause of mortality worldwide, making early detection crucial for reducing complications and improving outcomes. This study compares multiple Machine Learning and Deep Learning algorithms for heart disease classification using the Heart Disease dataset with 918 samples. It applies preprocessing such as feature normalization, an 80:20 stratified split, and lightweight hyperparameter tuning. Performance is evaluated with accuracy, precision, recall, F1-score, AUC, and confusion-matrix error analysis. Results show SVM and DNN achieving the top accuracies, while DNN carries higher computational cost and overfitting risk on small datasets.","A Comparative Study of Machine Learning and Deep Learning Models  \nfor Heart Disease Classification  \nMartina Sances Simanjuntak 1*, Robet 2*, Leony Hoki 3*  \n* Informatics Engineering, STMIK TIME, Medan, Indonesia  \n[martinaasancess@gmail.com](martinaasancess@gmail.com1)[1](martinaasancess@gmail.com1), [robertdetime@gmail.com](robertdetime@gmail.com2)[2](robertdetime@gmail.com2), [leonyhoki@gmail.com](leonyhoki@gmail.com3)[3](leonyhoki@gmail.com3)  \n\n| Article history:\u003Cbr>Received 2025-10-22 Revised 2025-11-20 Accepted 2025-11-22 | Heart disease remains one of the leading causes of mortality worldwide, necessitating accurate early detection. This study aims to compare the performance of several Machine Learning (ML) and Deep Learning (DL) algorithms in heart disease classification using the Heart Disease dataset with 918 samples. The methods tested included Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), and Deep Neural Network (DNN) . Preprocessing included feature normalization, data splitting (80:20), and simple hyperparameter tuning for parameter-sensitive models. Evaluations were conducted using accuracy, precision, recall, F1-score, AUC, and confusion matrix analysis to identify error patterns. The results showed that SVMand DNN achieved the highest accuracies of 91.3% and 92.1%, respectively. However, DNN has higher computational costs and risks of overfitting on small datasets. These findings confirm that traditional ML models such as SVM remain highly competitive on tabular medical data.\u003Cbr>\u003Cbr>This is an open access article under the CC–BY-SA license. |\n| --- | --- |\n| Keyword:\u003Cbr>Machine Learning, Deep Neural Network, Artificial Intelligence, Heart Disease, Classification. |  |\n\nArticle Info ABSTRACT  \nI. INTRODUCTION  \nHeart disease is a leading cause of mortality worldwide, including in Indonesia, where early detection plays a critical role in reducing complications and improving treatment outcomes [1] . According to the World Health Organization (WHO), cardiovascular diseases account for more than 17 million deaths annually [2] . And national surveys such as Riskesdas 2018 report a 1.5% prevalence of coronary heart disease in Indonesia [3] . Traditional diagnostic procedures, including electrocardiograms (ECGs), treadmill tests, and coronary angiography, remain costly, invasive, and less accessible in primary healthcare facilities [4] .  \nAdvances in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer promising alternatives for identifying patterns of heart disease from non-invasive clinical attributes such as age, blood pressure, cholesterol, and ECG characteristics [5] .  \nHowever, the effectiveness of these models depends greatly on data preprocessing, model selection, and evaluation strategies [6] . Previous studies have primarily focused on a limited set of algorithms, used only accuracy as  \nthe evaluation metric, or relied on international datasets without deeper analysis using metrics such as AUC or confusion matrix [7] [8] .  \nDespite the widespread availability of publicly accessible datasets, many of them, including Kaggle’s structured version of the Cleveland dataset, are not clinically validated and may contain sampling bias. This raises concerns about model generalizability, particularly for DL models that typically require larger datasets to avoid overfitting [9] .  \nMoreover, comparisons between classical ML methods and deep learning approaches on tabular medical data remain limited in the Indonesian literature [10] . To address these gaps, this study compares five widely used ML algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression with a Deep Neural Network (DNN) using standardized preprocessing and multimetric evaluation. Model performance is assessed using accuracy, precision, recall, F1-score, AUC, and confusion matrices to ","cbCaipRfmtfQDeSV","https://ap.wps.com/l/cbCaipRfmtfQDeSV","pdf",523540,1,5,"English","en",105,"# I. INTRODUCTION\n## AI for heart disease detection\n## Gaps in prior comparisons and evaluation\n# II. METHOD\n## Types of research\n## Research experiment flow","[{\"question\":\"What is the goal of the study on heart disease classification?\",\"answer\":\"The study aims to compare the performance of several Machine Learning and Deep Learning algorithms for heart disease classification using the Heart Disease dataset with 918 samples.\"},{\"question\":\"Which algorithms are evaluated in the experiment?\",\"answer\":\"The methods tested include Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and a Deep Neural Network (DNN).\"},{\"question\":\"How is model performance measured and compared?\",\"answer\":\"Models are assessed using accuracy, precision, recall, F1-score, AUC, and confusion-matrix analysis, after standardized preprocessing and a consistent train-test split with validation procedures.\"}]","A Comparative Study of Machine Learning and Deep Learning Models for Heart Disease Classification | PDF",1785899349,13,{"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},"a-comparative-study-of-machine-learning-and-deep-learning-models-for-heart-disease-classification","",{"@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/a-comparative-study-of-machine-learning-and-deep-learning-models-for-heart-disease-classification/125499/",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","2026-08-05",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},"What is the goal of the study on heart disease classification?","Question",{"text":75,"@type":76},"The study aims to compare the performance of several Machine Learning and Deep Learning algorithms for heart disease classification using the Heart Disease dataset with 918 samples.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithms are evaluated in the experiment?",{"text":80,"@type":76},"The methods tested include Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and a Deep Neural Network (DNN).",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured and compared?",{"text":84,"@type":76},"Models are assessed using accuracy, precision, recall, F1-score, AUC, and confusion-matrix analysis, after standardized preprocessing and a consistent train-test split with validation procedures.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]