[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120425-en":3,"doc-seo-120425-105":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},120425,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction and Analysis of Heart Failure using Machine Learning Techniques","Heart failure is a major cause of global mortality, driven by impaired myocardial function that reduces the heart’s ability to pump oxygenated, nutrient-rich blood. Traditional risk prediction methods often rely on statistical assumptions and struggle to model complex interactions in large, multidimensional patient datasets. This study applies supervised machine learning to enhance survival analysis and mortality prediction. A dataset of 1,000 patients with 12 features is evaluated using Naïve Bayes, decision trees, KNN, and random forests, with KNN yielding the strongest predictive accuracy.","| \u003Cbr>E-ISSN : 2988-585X (Online) | Journal of Elektronik Sistem InformasI\u003Cbr>(JESII)\u003Cbr>Volume 2 No 2 Desember 2024\u003Cbr>DOI : 10.31848/jesii.xxxx.xxxx |\n| --- | --- |\n\n\n| Prediction and Analysis of Heart Failure using Machine\u003Cbr>Learning Techniques\u003Cbr>Mehr Ali Qasimi1\u003Cbr>1Department of Information System, Badakhshan University, Afghanistan |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Oct 20, 24 Revised Dec 30, 24 Accepted Dec 31, 24\u003Cbr>Keywords:\u003Cbr>Analysis\u003Cbr>Classification Algorithms Heart Failure\u003Cbr>Machine Learning Prediction\u003Cbr>Corresponding Author: | Heart failure, a leading cause of global mortality with approximately 17.9 million deaths annually, arises from impaired myocardial function that hinders the heart's ability to pump oxygen-and nutrient-rich blood effectively. Current risk prediction methods are limited by their reliance on traditional statistical approaches, which fail to capture complex interactions in large, multidimensional datasets. This study aims to enhance survival analysis and mortality prediction for heart failure patients using machine learning techniques. A dataset of 1,000 patients with 12 key features was analyzed using supervised learning algorithms, including Naïve Bayes, decision trees, K-nearest neighbors (KNN), and random forests. The results revealed that KNN provided the highest predictive accuracy, effectively identifying significant patient characteristics associated with mortality risk. This approach demonstrates the potential of machine learning in improving prognosis accuracy and guiding early interventions. Future work should explore the inclusion of larger datasets, real-time applications, and advanced deep learning models to further refine predictive capabilities and support clinical decision-making. the integration of machine learning into heart failure prognosis enhances predictive accuracy and provides a scalable framework for leveraging multidimensional patient data. Future research should explore the inclusion of larger and more diverse datasets, real-time monitoring capabilities, and the application of deep learning models to further refine prediction efficacy and support clinical decision-making.\u003Cbr>ABSTRACT |\n| Mehr Ali Qasimi,\u003Cbr>Information System Department, Faculty of Computer Science, Badakhshan University. Badakhshan University, Badakhshan, Faizabad, Afghanistan. 3401\u003Cbr>Email: [q1.mehrali@gmail.com](q1.mehrali@gmail.com) |  |\n\n1. INTRODUCTION  \nHeart failure is an incurable illness. a dangerous condition when the heart's low cardiac output is the result of its inability to pump blood as effectively as it should. The ability of the heart to pump blood and supply the cells with oxygenated, nutrient-rich blood is crucial to the body's ability to operate properly[1]. Heart failure is divided into three categories based on how much blood is pumped out with each beat. Heart failure caused by left ventricular systolic dysfunction, or HFrEF, is represented by an EF \u003C 40% and is referred to as type 1 heart failure. Type 2 heart failure is defined as having an EF  \nbetween 40 and 49 percent and a mid-range ejection fraction (HfmrEF) . Heart failure type 3 is defined as diastolic heart failure with an EF of at least 50%, or heart failure with preserved ejection fraction, or HFpEF[2] . High blood pressure, smoking, obesity, diabetes, ischemic heart disease, and increased cholesterol are the main risk factors for developing the failing condition[3] . Depending on the age, gender, race, and ethnicity, heart failure can present with different characteristics.  \nThe evaluation of the patient's medical history, physical examination report, and analysis of concerning symptoms by medical professionals form the basis of invasive-based approaches for detecting heart disease. Because human error can lead to delays in diagnosis outcomes, all of these procedures typically result in erroneous diagnoses. Not only that, but it requires extra time and money for examination","cbCaimo4LMt8FxBR","https://ap.wps.com/l/cbCaimo4LMt8FxBR","pdf",687195,1,11,"English","en",105,"# Introduction\n## Heart failure types and risk factors\n## Challenges in diagnosis and need for early prediction\n## Role of machine learning and classification algorithms\n# Prediction and analysis approach\n## Supervised learning models and evaluation","[{\"question\":\"Why are current heart failure risk prediction methods limited?\",\"answer\":\"They often depend on traditional statistical approaches that cannot adequately capture complex interactions within large, multidimensional datasets.\"},{\"question\":\"Which dataset and features were used in the study?\",\"answer\":\"The study analyzed 1,000 patients with 12 key features for supervised learning–based prediction.\"},{\"question\":\"Which machine learning model achieved the highest predictive accuracy?\",\"answer\":\"K-nearest neighbors (KNN) provided the highest predictive accuracy and effectively identified patient characteristics linked to mortality risk.\"}]","Prediction and Analysis of Heart Failure using Machine Learning Techniques | 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