[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119360-en":3,"doc-seo-119360-105":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},119360,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","A Comparative Study Utilizing Machine Learning Algorithms to Predict Heart Disease in Young and Middle-Aged Adults","Early diagnosis is critical as heart disease becomes increasingly common worldwide. The study evaluates supervised machine-learning approaches for predicting cardiac illness using the Heart Disease dataset from Kaggle, aiming to support diagnostic applications with accurate classification. Models including Logistic Regression, Naive Bayes, Extreme Gradient Boost, K-Nearest Neighbor, Support Vector Classifier, Random Forest, and Decision Tree are assessed. Feature significance estimates are provided to identify key risk factors. The Binary Classification Neural Network achieves the highest testing accuracy above 90%, improving both prediction quality and interpretability.","A Comparative Study Utilizing Machine Learning Algorithms to Predict Heart Disease in Young and  \nMiddle-Aged Adults  \nCharu Kaushik  \nDept . Computer science and Engineering  \nManav Rachna International Institute of Research and Studies (MRIIRS)  \nFaridabad,Haryana,India  \n[charukaushik161263@gmail.com](charukaushik161263@gmail.com)  \nKamlesh Sharma  \nDept . Computer science and Engineering  \nManav Rachna International Institute of Research and Studies (MRIIRS)  \nFaridabad,Haryana,India  \n[associatedean_ks.academics@mriu.edu.in](associatedean_ks.academics@mriu.edu.in)  \nAbstract— Early diagnosis is crucial since heart disease is getting more and more common. In the field of medicine, machine learning algorithms are now used to predict cardiac and cardiovascular illness. examining and confirming the functionality of machine learning. Heart disease is becoming more and more commonplace worldwide. A multitude offactors impact the likelihood of a heart attack and other illnesses. In many countries, limited cardiovascular competency makes it difficult to predict complications related to heart disease. One way to predict the possibility of a heart disease-related issue is to use data mining and machine learning techniques to identify which machine learning classifiers are most accurate for various diagnostic applications. Several supervised machine-learning algorithms are evaluated for their effectiveness in predicting cardiac illness. Use the heart disease individual dataset available via Kaggle. This work employs several machinelearning algorithms, including. Using Logistic Regression (LR), Navie Bayes (NB), Extreme Gradient Boost (EGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Random Forest (RF), and Decision Tree (DT), a neural network is constructed. Capable of categorizing binary data. For every feature across all deployed, estimated feature significance ratings were supplied. Ways. This helps identify the main risk factors for heart disease in addition to increasing model accuracy and assisting in the best forecast. Lastly, in comparison to all machine learning methods and Neural. The Binary Classification Neural Network, as a network model, produced the highest testing accuracy of more than 90% .  \nKeywords-Machine Learning , Heart disease , Classification , Neural Network  \nI. INTRODUCTION  \nAn estimated 17.9 million people worldwide die each year from cardiovascular diseases (CVDs), which account for 31% of all deaths worldwide. Heart attacks and strokes are linked to four out of every five CVD deaths, and under-70s account for one third of these premature deaths. Eleven qualities in this dataset can be used to conjecture the probability of a cardiovascular condition. Heart failure is a common consequence of CVDs. A machine learning model can be very helpful in the early detection and treatment of cardiovascular disease and high-risk patients when one or more risk factors, such as diabetes, hypertension, hyperlipidemia, or an existing condition, are present.  \nMachine learning (ML)-based disease prediction is now possible thanks to the ever-increasing amount of medical data. In the medical field, ML techniques are frequently utilized. Several of the most well-known machine learning methods, such as the K-Nearest Neighbor, Random Forest, Naive Bayes classifier, Support Vector Machine, and Decision tree, were used in the study to predict heart disease. We also want to conduct a comparative analysis by contrasting the accuracy  \nmetrics ofthe ML algorithms used to predict heart disease. The dataset for the study was downloaded from Kaggle in the csv format and included data mining operations like data collection, data cleaning, data preprocessing, and exploratory data analysis. A comparison of the various machine learning methods used in categorization is provided in the research. With the dataset utilized in the review, the arbitrary timberland yielded the most noteworthy exactness rate, it was closed [1] ","cbCaiawcEVRAlf08","https://ap.wps.com/l/cbCaiawcEVRAlf08","pdf",1345283,1,13,"English","en",105,"# Introduction\n## Dataset and Risk Factors\n## Machine Learning for Heart Disease Prediction\n## Comparative Evaluation and Metrics","[{\"question\":\"Why is early heart disease diagnosis important in this study?\",\"answer\":\"Early diagnosis helps identify high-risk patients and supports timely preventive treatment by estimating the likelihood of cardiac conditions before complications occur.\"},{\"question\":\"Which dataset is used for training and comparison?\",\"answer\":\"The study uses the Heart Disease individual dataset available via Kaggle in csv format, followed by data cleaning, preprocessing, and exploratory data analysis.\"},{\"question\":\"How are the machine learning models compared?\",\"answer\":\"Multiple supervised classifiers are evaluated using comparative accuracy metrics, and the study also references feature correlation analysis and feature significance to support feature selection.\"}]","A Comparative Study Utilizing Machine Learning Algorithms to Predict Heart Disease in Young and Middle-Aged Adults | 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