[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118370-en":3,"doc-seo-118370-105":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},118370,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Comparative analysis of heart failure prediction using machine learning models - Research report","The study addresses the early prediction of heart failure, a major global cause of hospitalization and death, by leveraging machine learning to analyze large-scale medical and lifestyle-related data. Classification approaches are compared using models such as logistic regression, K-nearest neighbor, support vector machines, decision trees, and random forests. By capturing non-linear relationships among risk factors, the models estimate individual risk and support timely intervention. Performance evaluation is used to assess predictive effectiveness and inform more personalized prevention and treatment strategies.","Comparative analysis of heart failure prediction using machine  \nlearning models  \nSrinivas Kanakala, Vempaty Prashanthi  \nDepartment ofCSE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India  \n\n| Article history:\u003Cbr>Received Feb 4, 2024 Revised May 1, 2024 Accepted May 12, 2024 |\n| --- |\n| Keywords:\u003Cbr>Classification Confusion matrix Decision tree\u003Cbr>K-nearest neighbor Logistic regression Machine learning Naive Bayes prediction Random forest |\n\nCorresponding Author:  \nSrinivas Kanakala  \nHeart failure is a critical health problem worldwide, and its prediction is a major challenge in medical science. Machine learning has shown great potential in predicting heart failure by analyzing large amounts of medical data. Heart failure prediction with the help of machine learning classification algorithms involves the use of models such as decision trees, logistic regression, and support vector machines to identify and analyze potential risk factors for heart failure. By analyzing large datasets containing medical and lifestyle-related variables, these models can accurately predict the likelihood of heart failure occurrence in individuals. In our research, the heart failure prediction and comparison are done using logistic regression, K-nearest neighbor (KNN), support vector machines (SVM), decision tree and random forest The accurate identification of high-risk individuals enables early intervention and better management of heart failure, reducing the risk of mortality and morbidity associated with this condition. Overall, machine learning algorithms play a major role in improving the accuracy of heart failure risk assessment, allowing for more personalized and effective prevention and treatment strategies.  \nThis is an open access article under the CC BY-SA license.  \nDepartment ofCSE  \nVNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, Telangana, India  \nEmail: [srinivaskanakala@gmail.com](srinivaskanakala@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nHeart failure is a widespread and serious condition where the heart struggles to pump an adequate amount of blood to fulfill the body's requirements. It impacts millions globally and stands as a primary reason for hospital admissions and deaths. Recognizing and foreseeing heart failure promptly can significantly enhance patient results and decrease healthcare expenses. Over recent years, the utilization of machine learning (ML) methods has displayed potential in assisting with predicting and detecting heart failure at an early stage. By leveraging large datasets and powerful algorithms, ML algorithms can extract patterns and insights from patient data, facilitating accurate predictions and personalized treatment strategies.  \nMachine learning algorithms possess the capability to analyze extensive sets of patient data, encompassing medical records, imaging scans, genetic profiles, and lifestyle details. These algorithms can recognize hidden patterns and relations between the data that may not be evident to human observers. By considering multiple variables simultaneously, ML algorithms can create predictive models that estimate an individual's risk of developing heart failure or worsening of their condition over time.  \nOne notable benefit of machine learning in predicting heart failure is its capacity to manage intricate, non-linear connections among various risk factors.. Traditional statistical models often assume  \nlinearity, limiting their effectiveness in capturing the intricate interactions that contribute to heart failure. ML algorithms, on the other hand, can model these intricate relationships, incorporating multiple variables and their interactions to provide more accurate predictions. This capability allows for a more complete understanding of the underlying risk factors and aids in the development of personalized treatment plans.  \nIn their analysis [1], data mining techniques such as K-nearest neighbor, T3 Algorithm, an","cbCaivbnnmNd9Plq","https://ap.wps.com/l/cbCaivbnnmNd9Plq","pdf",552868,1,9,"English","en",105,"# Introduction\n## Machine learning for early risk prediction\n## Modeling non-linear risk-factor relationships\n## Related work and prior methods","[{\"question\":\"Which machine learning models are compared for heart failure prediction?\",\"answer\":\"The research compares logistic regression, K-nearest neighbor (KNN), support vector machines (SVM), decision tree, and random forest.\"},{\"question\":\"How does machine learning improve prediction compared with traditional statistical methods?\",\"answer\":\"Machine learning can model complex, non-linear interactions among multiple risk factors, which traditional statistical models often assume to be linear.\"},{\"question\":\"What role does early identification of high-risk patients play?\",\"answer\":\"Accurate identification enables early intervention and better management, helping reduce mortality and morbidity associated with heart failure.\"}]","Comparative analysis of heart failure prediction using machine learning models - 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