[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119301-en":3,"doc-seo-119301-105":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},119301,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Early detection of coronary heart disease based on risk factors using interpretable machine learning","Coronary heart disease (CHD) is a leading cause of death, and early detection can reduce or prevent risk. This study proposes an early CHD detection model that improves algorithm interpretability by using the C5.0 algorithm and explaining individual factor contributions with Shapley additive explanations (SHAP). The workflow includes preprocessing, interpretable machine learning, and performance evaluation. Using 215 patient records from Dr. Moewardi Surakarta Hospital and k-fold cross-validation, the model achieves 84.64% accuracy and highlights systolic blood pressure, diastolic blood pressure, and employment level as key contributors.","Early detection of coronary heart disease based on risk factors using interpretable machine learning  \nWiharto, Farah Nada Mufidah  \nDepartment of Informatics, Faculty of Information Technology and Data Science, Universitas Sebelas Maret, Surakarta, Indonesia  \n\n| Article history:\u003Cbr>Received Nov 6, 2023 Revised Jul 9, 2024 Accepted Aug 25, 2024 | Coronary heart disease (CHD) is the leading cause of death in the world. The risk of coronary heart disease can be reduced or even prevented by early detection. Early detection ofCHD has been widely developed using machine learning, but the machine learning algorithms used sometimes have low interpretability. Low interpretability makes it difficult for users to understand the cause of the decision. Referring to this, this research aims to propose an early detection model using machine learning interpretability, which is implemented using the C5.0 algorithm and interpreted using Shapley additive explanations (SHAP) . This research method is divided into 3 stages, namely preprocessing, interpretable machine learning, and performance evaluation. This study used 215 patient data from Dr. Moewardi Surakarta Hospital. Testing the resulting model using the k-folds cross-validation method. The test results show that the risk factors that make a high contribution to the output of the coronary heart disease detection model are systolic blood pressure, diastolic blood pressure, and employment level, with the resulting accuracy performance of 84.64% . The proposed model can be an alternative for early prediction of coronary heart disease which can explain the influence of each selected risk factor on the model output.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>C5.0 algorithm\u003Cbr>Coronary heart disease Early detection\u003Cbr>Interpretable machine learning Risk factors |  |\n\nCorresponding Author:  \nWiharto  \nDepartment of Informatics, Faculty of Information Technology and Data Science Universitas Sebelas Maret  \nIr. Sutami St., No. 36A, Kentingan, Jebres, Surakarta, Indonesia [Email: wiharto@staff.uns.ac.id](Email: wiharto@staff.uns.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCoronary heart disease (CHD) is a leading global cause of death, with more than 9 million deaths attributed to CHD in 2020 [1] . The COVID-19 pandemic has worsened this already dire situation. Although the mortality rate due to COVID-19 varies worldwide, ranging from 1-2%, most patients can recover. Nonetheless, a considerable amount of evidence indicates that COVID-19 can cause various long-term health issues, including one that heightens the possibility of heart problems. A comprehensive study of health records in the United States revealed that individuals who contracted COVID-19 have a 55% greater chance of experiencing extended cardiovascular complications. Complications of coronary heart disease consist of heart rhythm disturbances, heart inflammation, blood clots, stroke, heart attack, heart failure, and possibly death. The sample registration system (SRS) survey from 2014 determined that coronary heart disease has the second highest mortality rate in Indonesia, following stroke, with a total mortality rate of 12.9% of all recorded deaths. Coronary artery disease is the primary and most prevalent cause of mortality in Indonesia, accounting for 26.4% of all deaths. The number of deaths attributed to heart disease outweighs the number of  \nheart and blood vessel specialists by a significant margin in Indonesia, with only 600 currently practicing in the country.  \nCoronary artery disease (CAD) is caused by the buildup of plaque in the coronary arteries, leading to decreased blood flow to the heart and increasing the risk of heart attacks and even mortality. Early awareness of a person's likelihood of experiencing CAD can decrease risk. Regular monitoring of CAD risk factors, such as cholesterol levels, blood sugar, blood pressure, and weight, is helpful for early det","cbCaibJMsyGvNosG","https://ap.wps.com/l/cbCaibJMsyGvNosG","pdf",798526,1,13,"English","en",105,"# Article history\n# Abstract\n# Keywords\n# Introduction\n## Global burden of CHD and COVID-19 impact\n## Risk factors and existing prediction models\n## Limitations of population-based models\n## Machine learning approaches for CHD prediction","[{\"question\":\"What problem does the research address in CHD early detection?\",\"answer\":\"Traditional machine learning models can have low interpretability, making it difficult to understand why a decision is made for CHD detection.\"},{\"question\":\"Which interpretability method is used to explain the model outputs?\",\"answer\":\"The study uses Shapley additive explanations (SHAP) to interpret the contribution of each selected risk factor.\"},{\"question\":\"What risk factors show high contribution in the proposed model and what accuracy is achieved?\",\"answer\":\"Systolic blood pressure, diastolic blood pressure, and employment level contribute highly, and the model achieves 84.64% accuracy using k-fold cross-validation.\"}]","Early detection of coronary heart disease based on risk factors using interpretable machine learning | 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problem does the research address in CHD early detection?","Question",{"text":75,"@type":76},"Traditional machine learning models can have low interpretability, making it difficult to understand why a decision is made for CHD detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which interpretability method is used to explain the model outputs?",{"text":80,"@type":76},"The study uses Shapley additive explanations (SHAP) to interpret the contribution of each selected risk factor.",{"name":82,"@type":73,"acceptedAnswer":83},"What risk factors show high contribution in the proposed model and what accuracy is achieved?",{"text":84,"@type":76},"Systolic blood pressure, diastolic blood pressure, and employment level contribute highly, and the model achieves 84.64% accuracy using k-fold 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