[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126045-en":3,"doc-seo-126045-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126045,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","The Effectiveness of Data Imputations on Myocardial Infarction Complication Classification Using Machine Learning Approach - Hyperparameter Tuning","Myocardial infarction complications are a life-threatening emergency driven by blocked coronary blood flow, typically caused by a clot in an artery narrowed by atherosclerotic plaque. Accurate diagnosis combines physical assessment, ECG interpretation, blood enzyme testing, and imaging such as coronary angiography. To reduce adverse outcomes, this study builds early prediction models using machine learning classification on clinical records with missing values handled via KNN, iterative, and MissForest imputations. Hyperparameters are optimized with Bayesian optimization, and results show iterative imputation achieves the best performance, with SVM reaching perfect accuracy and XGBoost attaining near-complete accuracy.","The Effectiveness of Data Imputations on Myocardial Infarction Complication Classification Using Machine Learning Approach  \nwith Hyperparameter Tuning  \nMuhammad Itqan Mazdadi, Triando Hamonangan Saragih, Irwan Budiman, Andi Farmadi, Ahmad Tajali Department of Computer Science, Lambung Mangkurat University, Jalan A. Yani Km 36, Banjarbaru 70714, Indonesia  \nARTICLE INFO  \nArticle history:  \nReceived July 17, 2024 Revised August 14, 2024 Published September 04, 2024  \nKeywords:  \nMyocardial Infarction; Machie Learning; Classification;  \nData Imputation; Bayesian Optimization  \nCorresponding Author:  \nABSTRACT  \nComplications from Myocardial Infarction (MI) represent a critical medical emergency caused by the blockage of blood flow to the heart muscle, primarily due to a blood clot in a coronary artery narrowed by atherosclerotic plaque. Diagnosing MI involves physical examination, electrocardiogram (ECG) evaluation, blood sample analysis for specific heart enzyme levels, and imaging techniques such as coronary angiography. Proactively predicting acute myocardial complications can mitigate adverse outcomes, and this study focuses on early prediction using classification methods. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost were employed to classify patient medical records accurately. Techniques like K-Nearest Neighbors (KNN) imputation, Iterative imputation, and Miss Forest were used to handle incomplete datasets, preserving vital information. Hyperparameter optimization, crucial for model performance, was performed using Bayesian Optimization, which minimizes the objective function by modeling past evaluations. The contribution to this study is to see how much influence data imputation has on classification using machine learning methods on missing data and to see how much influence the optimization method has when performing hyperparameter tuning. Results demonstrated that the Iterative Imputation method yielded excellent performance with SVM and XGBoost algorithms. SVM achieved 100% accuracy, precision, sensitivity, F1 score, and AUC. XGBoost reached 99.4% accuracy, 100% precision, 79.6% sensitivity, an F1 score of 88.7%, and an AUC of 0.898. KNN Imputation with SVM showed results similar to Iterative Imputation with SVM, while Random Forest exhibited poor classification outcomes due to data imbalance, causing overfitting.  \nThis work is licensed under a Creative Commons Attribution-Share Alike 4.0  \nTriando Hamonangan Saragih, Department of Computer Science, Lambung Mangkurat University, Jalan A. Yani Km 36, Banjarbaru 70714, Indonesia  \nEmail: [triando.saragih@ulm.ac.id](triando.saragih@ulm.ac.id)  \n1. INTRODUCTION  \nComplications from Myocardial Infarction (MI) constitute a critical medical emergency precipitated by the obstruction of blood flow to the heart muscle [1] . This blockage occurs when coronary arteries, responsible for supplying blood to the heart, become suddenly blocked, primarily due to a blood clot within an artery narrowed by the accumulation of atherosclerotic plaque [2] . Consequently, the segment of the heart muscle deprived of adequate blood supply begins to experience cellular death due to the lack of oxygen and essential nutrients [3] . Classic symptoms of MI complications include intense chest discomfort, difficulty in breathing, nausea, and vomiting [4] . Diagnosis involves a comprehensive evaluation, including physical examination, electrocardiogram (ECG) assessment, blood sample analysis for specific heart enzymes, and often imaging techniques such as coronary angiography [5] . Prompt therapeutic interventions are crucial to mitigate irreversible cardiac injury and improve patient prognosis. These interventions may include thrombolytic agents  \nto dissolve the clot, coronary procedures like angioplasty or coronary artery bypass graft surgery, and longterm therapeutic regimens to prevent recurrent MI incidents. Proactive anticipation of acute myocar","cbCaiv27J5gZRxIx","https://ap.wps.com/l/cbCaiv27J5gZRxIx","pdf",946615,5,1,14,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is predicting myocardial infarction complications important?\",\"answer\":\"Predicting acute myocardial infarction complications supports earlier clinical decision-making and can mitigate irreversible cardiac injury, improving patient prognosis.\"},{\"question\":\"How does the study address missing data in patient records?\",\"answer\":\"It applies data imputation methods including KNN imputation, iterative imputation, and MissForest to preserve important information in incomplete datasets.\"},{\"question\":\"Which imputation and model combination performed best?\",\"answer\":\"Iterative imputation produced the strongest results: SVM achieved 100% accuracy and related metrics, while XGBoost reached 99.4% accuracy with high overall performance.\"}]","The Effectiveness of Data Imputations on Myocardial Infarction Complication Classification Using Machine Learning Approach - 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