[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121161-en":3,"doc-seo-121161-105":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":20,"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},121161,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Evaluation of machine learning techniques for hypertension risk prediction based on medical data in Bangladesh","Hypertension in Bangladesh is a major driver of cardiovascular disease, stroke, and kidney failure, causing substantial illness and premature mortality. This study applies multiple machine learning models, including naive Bayes, support vector machine, logistic regression, and random forest, to predict hypertension risk in individuals identified as high risk. A hybrid approach reaches 78.17% prediction accuracy, exceeding individual baselines, with random forest achieving 73.86% accuracy. Model quality is assessed via sensitivity, specificity, precision, F-score, ROC analyses, and confusion matrices using 10-fold cross-validation.","Evaluation of machine learning techniques for hypertension risk prediction based on medical data in Bangladesh  \nMd. Asadullah1, Md. Murad Hossain1,2, Sabrina Rahaman1, Muhammad Saad Amin3, Mst. Sharmin Akter Sumy4,5, Md. Yasin Ali Parh4,5, Mohammad Amzad Hossain6  \n1Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University ,  \nGopalganj, Bangladesh  \n2Modeling and Data Science Program, University of Turin, Turin, Italy  \n3Department of Computer Science, University of Turin, Turin, Italy  \n4Department of Statistics, Islamic University, Kushtia, Bangladesh  \n5Department of Bioinformatics and Biostatistics, University of Louisville, Kentucky, USA 6Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh  \nArticle history:  \nReceived Sep 11, 2022 Revised Jun 4, 2023 Accepted Jun 17, 2023  \nKeywords:  \nClassification Hypertension Machine learning Performance Receiver operating characteristic  \nCorresponding Author:  \nHypertension in Bangladesh is a leading cause of cardiovascular diseases, stroke, and kidney failure, resulting in significant morbidity and mortality. Preventive measures and simple health practices can effectively reduce hypertension and its complications. This study utilizes machine learning algorithms (naive Bayes, support vector machine, logistic regression, random forest) to predict hypertension in high-risk individuals. The proposed hybrid model achieves a prediction accuracy of 78.17%, surpassing other machine learning methods. Random forest has the highest accuracy among the individual algorithms at 73.86% . Classification performance is evaluated using sensitivity, specificity, precision, and F-score, along with receiver operating characteristic analyses and confusion matrices through 10-fold cross-validation. These findings emphasize the importance of managing risk factors for better population health and highlight the efficacy of the hybrid model in hypertension prediction. The study underscores the significance of preventive measures in reducing the burden of hypertension-related diseases and improving overall well-being.  \nThis is an open access article under the CC BY-SA license.  \nMd. Murad Hossain  \nDepartment of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj-8100, Bangladesh  \nEmail: mdmurad.hossain@unito.it  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nAn educational clinical center used statistical and machine learning techniques to examine the digital health records of 14,360 adult hypertension patients. Finding predictors and the timing of lifestyle modifications was the goal [1] . Using data from Kuwait, the study developed classification models and risk assessment tools for diabetes, high blood pressure, and comorbidities [2] . Numerous techniques were used, including support vector machines (SVMs), multifactor dimensionality reduction, logistic regression, and Knearest neighbors (KNNs) . Fivefold cross-validation was used to get generalization errors and accuracies [3] . In this work, pulse waves from both the hypertensive and healthy groups were classified and predicted using a machine learning technique. By removing noise with K-means, the goal was to evaluate how pulse waves affected the accuracy and stability of the machine learning model [4] . In order to enhance therapy personalization and patient outcomes, the study used decision trees and neural networks to uncover parameters that contribute to the effectiveness of high blood pressure medicine treatment for a broad group of  \npatients [5] . The study used supervised principal component analysis to identify systolic motion patterns that were highly predictive of survival. The researchers assessed the precision of survival prediction using the area under the curve with time-structured receiver operating characteristic analysis for 1-year survival [6] . The aim of the study was to assess the perform","cbCaijAB8PezRuGD","https://ap.wps.com/l/cbCaijAB8PezRuGD","pdf",465401,1,9,"English","en",105,"# Introduction\n## Machine learning methods for hypertension prediction\n## Evaluation metrics and validation approach","[{\"question\":\"Which machine learning algorithms are used for hypertension risk prediction?\",\"answer\":\"The study uses naive Bayes, support vector machine, logistic regression, and random forest, and also evaluates a hybrid model combining approaches.\"},{\"question\":\"What model performs best, and what accuracy does it achieve?\",\"answer\":\"The proposed hybrid model achieves 78.17% prediction accuracy, outperforming other evaluated machine learning methods.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is measured using sensitivity, specificity, precision, F-score, ROC analyses, and confusion matrices, with 10-fold cross-validation for generalization.\"}]","Evaluation 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machine learning algorithms are used for hypertension risk prediction?","Question",{"text":75,"@type":76},"The study uses naive Bayes, support vector machine, logistic regression, and random forest, and also evaluates a hybrid model combining approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What model performs best, and what accuracy does it achieve?",{"text":80,"@type":76},"The proposed hybrid model achieves 78.17% prediction accuracy, outperforming other evaluated machine learning methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated in the study?",{"text":84,"@type":76},"Performance is measured using sensitivity, specificity, precision, F-score, ROC analyses, and confusion matrices, with 10-fold cross-validation for 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