[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121420-en":3,"doc-seo-121420-105":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},121420,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions - Research Article","This research develops machine-learning classification models to predict cardiovascular disease outcomes, emphasizing an ensemble strategy that combines Random Forest and Gradient Boosting with additional learning components. The approach integrates Linear Regression, Random Forest, and Gradient Boosting algorithms, tuned through Bayesian hyperparameter optimization to improve generalization. Reported results show strong classification accuracy across methods, with the Gradient Boosted (GB) algorithm achieving a true positive rate of 98.3%. The study also frames a hypothesis comparing GB performance on the Framingham dataset using 4,240 samples.","An Acad Bras Cienc (2025) 97(3): e20240811 DOI 10.1590/0001-3765202520240811  \nAnais da Academia Brasileira de Ciências | Annals of the Brazilian Academy of Sciences Printed ISSN 0001-3765 I Online ISSN 1678-2690 [www.scielo. br/aabc | www.fb.com/aabcjournal](www.scielo. br/aabc | www.fb.com/aabcjournal)  \nENGINEERING SCIENCES  \nPredicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions  \nVIJAI M. MOORTHY, BHUPAL N. DHARAMSOTH, VIJAYALAKSHMI MUTHUKARUPPAN, ARUL ELANGO & KALAIARASI GANESAN  \nAbstract: This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradient Boosting. The authors developed a novel ensemble learning model, combining Linear Regression, Random Forest, and Gradient Boosting algorithms, optimized using Bayesian hyperparameter tuning. The model demonstrated superior performance in predicting CVD outcomes, with classification accuracy of 95.5%, 94.26%, and 98.3% for Linear Regression, Decision Tree, and Gradient Boosted methods, respectively. The true positive rate for the GB algorithm’s predictions of patients was 98.3% . The study hypothesizes that the GB method predicts the Framingham dataset better than other algorithms using 4240 samples.  \nKey words: machine learning, data science, LR, Decision Tree, GB, algorithm.  \nINTRODUCTION  \nTwenty-two million people lose their lives annually as a result of cardiovascular disease, as indicated by the statistics that are provided by the World Health Organization. Death and disability from heart disease are major global health problems. One of the most crucial issues in data analysis is the ability to foresee cardiovascular illness. Overall, the prevalence of cardiovascular disease has been rising ata rate that has been alarmingly high in the previous few years. Many studies have been alot of studies aimed at determining what causes heart disease and how to forecast someone’s  \nrisk. It is also noted that heart disease is a major cause of mortality without visible symptoms. A reduction in complications can be achieved by modifications in lifestyle that can be implemented after an early diagnosis of heart disease in those at high risk. Experiments  \nand research have been conducted during the past several years, since medical science, Data Science, and Machine learning techniques have experienced rapid growth in recent decades (Friede et al. 1996, Rosamond et al. 2008, Hajar 2017) .  \nMelillo et al. (2013) employed the machine learning method (Classification and Regression Trees (CART) to predict cardiac disease. Numerous studies have been carried out, and various machine learning models are now being utilized to assist in the classification and prediction of cardiac disease. Patients at high risk for congestive heart failure and those at low risk are separated using an algorithmic classifier. They obtained 93.3% sensitivity and 63.5% specificity using CART algorithm.  \nPurushottam et al. (2016) devised an efficient algorithm based on hill climbing and to evaluate the probability of heart disease, decision trees were used. To employ classification techniques,  \nAn Acad Bras Cienc (2025) 97(3)  \nthey pre-processed the data from the Cleveland dataset. In order to complete the data set, missing values are filled in using Evolutionary Learning (KEEL), a data mining open-source method is used for information extraction. There is a strict hierarchy in a decision tree. At each stage, the hill-climbing algorithm selects a candidate node by testing. Confidence has been placed in the parameters and their values. It has a confidence level of at least 0.25. The method is approximately 86.7% accurate.  \nKeogh & Mueen (2017) later proposed an ECGbased strategy for performance enhancement. A clinical decision support system for the early identification of heart failure is d","cbCaip7WtpaYfF7T","https://ap.wps.com/l/cbCaip7WtpaYfF7T","pdf",808958,1,12,"English","en",105,"# Introduction\n## Cardiovascular disease burden and need for prediction\n## Prior machine-learning approaches for heart disease\n## Data challenges and dimensionality reduction\n## Cleveland and Framingham datasets in previous studies","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To predict cardiovascular disease outcomes using machine-learning classification methods, with a focus on improving performance through ensemble learning and hyperparameter tuning.\"},{\"question\":\"Which modeling techniques are integrated in the proposed approach?\",\"answer\":\"The study combines Linear Regression, Random Forest, and Gradient Boosting, optimized using Bayesian hyperparameter tuning.\"},{\"question\":\"How does the paper evaluate model performance?\",\"answer\":\"Performance is assessed using classification accuracy for multiple methods and true positive rate for the Gradient Boosted (GB) algorithm, including results based on the Framingham dataset.\"}]","Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions - 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