[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124811-en":3,"doc-seo-124811-105":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},124811,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Enhancing coronary artery diseases screening - A comprehensive assessment of machine learning approaches using routine clinical and laboratory data","Coronary artery disease (CAD) is a leading global cause of mortality, making early detection essential for timely treatment. While Coronary Angiography (CA) remains the diagnostic gold standard, its invasiveness, cost, and side effects limit its usefulness for screening. This study develops and compares machine learning classifiers using routine clinical and laboratory data, applying feature selection (LASSO, ReliefF), multiple performance metrics, and SHAP-based interpretability. Results support non-invasive CAD screening and highlight key predictive features.","Enhancing coronary artery diseases screening: A comprehensive assessment of machine learning approaches using routine clinical and laboratory data  \nShahryar Naji 1, Zahra Niazkhani2,3, Kamal Khademvatan4, Habibollah Pirnejad3,5*  \n1Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran  \n2Nephrology and Kidney Transplant Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran  \n3Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam, The Netherlands  \n4Department of Cardiology, Urmia University of Medical Sciences, Urmia, Iran  \n5Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran  \n\n| Article Info | A B S T R A C T |\n| --- | --- |\n| Article type:\u003Cbr>Research | Introduction: Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data.\u003Cbr>Material and Methods: Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, |\n| Article History:\u003Cbr>Received: 2023-12-03\u003Cbr>Accepted: 2024-03-22\u003Cbr>Published: 2024-04-13 |  |\n| * Corresponding author:\u003Cbr>Habibollah Pirnejad |  |\n| Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran | support vector machine, logistic regression, and stacked ensemble learning were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, |\n| [Email: pirnejad@eshpm.eur.nl](Email: pirnejad@eshpm.eur.nl) | sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model.\u003Cbr>Results: The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold crossvalidation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90. 38%, 90 . 07%, and 90 . 39%, sensitivities of 94 .43%, 93. 03%, and 93 . 96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88. 79%, 88. 77%, and 90. 06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model.\u003Cbr>Conclusion: The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the ZAlizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings. |\n| Keywords:\u003Cbr>Coronary Artery Disease Coronary Angiography Machine Learning Artificial Intelligence |  |\n| Cite this paper as:\u003Cbr>Naji S, Niazkhani Z, Khademvatan K, Pirnejad H. Enhancing coronary artery diseases screening: A comprehensive assessment of machine learning approaches using routine clinical and laboratory data. Front Health Inform. 2024; 13: 202. DOI:\u003Cbr>10.30699/fhi.v13i0.555 |  |\n\nINTRODUCTION  \nCoronary artery disease (CAD) is a common chronic disease in which the coronary arteries become  \nnarrowed by atherosclerosis, resulting in an inadequate blood supply to the heart mus","cbCaihWIHZeQzxMg","https://ap.wps.com/l/cbCaihWIHZeQzxMg","pdf",1003705,1,15,"English","en",105,"# Introduction\n## Coronary artery disease burden and need for early diagnosis\n## Limitations of coronary angiography\n## Non-invasive alternatives and rationale for machine learning\n# Materials and Methods\n## Machine learning models and classification setup\n## Feature selection methods\n## Evaluation metrics and model interpretability","[{\"question\":\"Why is early CAD screening important despite coronary angiography being the gold standard?\",\"answer\":\"Early screening enables timely treatment and reduces fatal outcomes. Coronary angiography, although accurate, is invasive, costly, and associated with side effects, making it less suitable for screening large populations.\"},{\"question\":\"Which machine learning approaches are used to differentiate CAD and healthy subjects?\",\"answer\":\"The study employs multiple classifiers including decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and a stacked ensemble learning approach.\"},{\"question\":\"How did the study determine which features mattered most?\",\"answer\":\"Feature selection was performed using LASSO and ReliefF, and the SHAP technique was used to interpret the artificial neural network model and identify influential clinical features.\"}]","Enhancing coronary artery diseases screening - A comprehensive assessment of machine learning approaches using routine clinical and laboratory data | PDF",1785894788,38,{"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},"enhancing-coronary-artery-diseases-screening-a-comprehensive-assessment-of-machine-learning-approaches-using-routine-clinical-and-laboratory-data","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-coronary-artery-diseases-screening-a-comprehensive-assessment-of-machine-learning-approaches-using-routine-clinical-and-laboratory-data/124811/",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},"Why is early CAD screening important despite coronary angiography being the gold standard?","Question",{"text":75,"@type":76},"Early screening enables timely treatment and reduces fatal outcomes. Coronary angiography, although accurate, is invasive, costly, and associated with side effects, making it less suitable for screening large populations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approaches are used to differentiate CAD and healthy subjects?",{"text":80,"@type":76},"The study employs multiple classifiers including decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and a stacked ensemble learning approach.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the study determine which features mattered most?",{"text":84,"@type":76},"Feature selection was performed using LASSO and ReliefF, and the SHAP technique was used to interpret the artificial neural network model and identify influential clinical features.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]