[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124141-en":3,"doc-seo-124141-105":30,"detail-sidebar-cat-0-en-105":92},{"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},124141,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Hybrid Feature Selection with Data-Driven Approach for Cardiovascular Disease Prediction Using Machine Learning","A hybrid feature selection framework is presented for cardiovascular disease prediction using machine learning to improve clinical decision support. The approach addresses performance degradation when training data quality is inconsistent by enhancing training quality through feature selection. A new hybrid feature selection algorithm integrates multiple filter methods to reduce model parameters and strengthen the feature set. The proposed cardiovascular disease prediction framework (CVDPF) combines the hybrid feature selection (HFS) with machine learning tools to form ML-CVDP. Experiments on a cardiovascular disease dataset show that CVDPF with HFS outperforms other available feature selection methods.","A hybrid feature selection with data-driven approach for cardiovascular disease prediction using machine learning  \nThoutireddy Shilpa, Rajib Debnath  \nDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India  \nArticle history:  \nReceived Mar 21, 2024 Revised Oct 18, 2024 Accepted Nov 14, 2024  \nKeywords:  \nCardiovascular disease prediction Clinical decision support system Feature selection  \nHybrid feature selection Machine learning  \nCorresponding Author:  \nAffecting various disorders of heart and blood vessels mainly cardiovascular diseases (CVDs) is the leading cause of human mortality on the planet. A number of machine learning (ML) based supervised learning approaches existing in the literature have been found useful in the clinical decision support system (CDSS) for detecting CVDs automatically. The challenge, however, is that their performance tends to decline unless the training data is ofa certain standard. Several approaches to solving this problem are known as featureselection techniques. Despite several notable advancements in the CVD modeling literature, a weak compendium of research exists in an area which supports the integration of the feature selection approach as a means of enhancing the training quality and thus the prediction accuracy. Against this background, in this paper, we proposed a framework called the cardiovascular disease prediction framework (CVDPF) that integrates ML methods. To support this, we designed and proposed a new hybrid feature selection (HFS) algorithm that aims to reduce the number of parameters. This algorithm adopts several filter methods in order to enhance its performance for the task of feature selection. To improve the prediction accuracy of CVDs, a number of ML tools using the HFS approach has been designed and is termed as machine learning based cardiovascular disease prediction (ML-CVDP) . The validation of the framework and the algorithms discussed has been done on the basis of a CVD dataset. The experimental findings demonstrated that CVDPF in combination with HFS outperforms other methods of feature selection available.  \nThis is an open access article under the CC BY-SA license.  \nRajib Debnath  \nDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana 500075, India  \nEmail: [rajibdebnath.cse@gmail.com](rajibdebnath.cse@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nKolukisaet et al. [1] introduced a combined feature selection methodology in diagnosing risks of coronary heart disease (CHD) . Ghosh et al. [2] reviewed modelling Lasso and Relief based feature selection methods with machine learning (ML)-based methods in prediction of cardiovascular diseases (CVD) . Mohan et al. [3] put forth a proposal for feature excludings and assessment of some CVD prediction ML techniques. Nourmohammadi-Khiaraket et al. [4] presented advanced techniques concerning feature selection to enhance the diagnostic system for the health care system against heart diseases. Nasarian et al. [5] presented a heterogeneous hybrid methodology concerning feature selection and diagnosis of coronary artery disease through ML models. To address the problem of heart disease Long et al. [6] created a more efficient approach using firefly optimization techniques. Ahmed et al. [7] investigated self-aggregating cultural framing among users of health-focused social media to unearth myocardial infarction. Jain and Singh [8] made use of a variety  \nof feature selection and classification techniques in predicting chronic diseases. Yekkala et al. [9] were the first to predict heart disease in an enhanced way using an ensemble approach and particle swarm optimization (PSO) technique. Farooq and Hussain [10] suggested a technique called ‘machine learning [11] driven prognostic system (MLDPS)’ to treat patients with CVDs. Meenakshi et al. [12] applied supervised ML techniques, including support vector machines","cbCaitQR9zbQIZlE","https://ap.wps.com/l/cbCaitQR9zbQIZlE","pdf",639668,1,9,"English","en",105,"# Abstract\n# Introduction\n## Related Work in Cardiovascular Disease Prediction\n## Feature Selection Techniques and Machine Learning Models","[{\"question\":\"What problem does the proposed framework address in cardiovascular disease prediction?\",\"answer\":\"It targets the performance decline of supervised machine learning models when training data is not of a certain standard, by improving training quality through feature selection.\"},{\"question\":\"How does the new hybrid feature selection algorithm work?\",\"answer\":\"It integrates several filter methods to enhance feature selection performance and reduce the number of parameters.\"},{\"question\":\"How was the framework validated and what were the results?\",\"answer\":\"Validation used a cardiovascular disease dataset, and results indicate that CVDPF combined with HFS outperforms other feature selection methods.\"}]","A Hybrid Feature Selection with Data-Driven Approach for Cardiovascular Disease Prediction Using Machine Learning | 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problem does the proposed framework address in cardiovascular disease prediction?","Question",{"text":76,"@type":77},"It targets the performance decline of supervised machine learning models when training data is not of a certain standard, by improving training quality through feature selection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the new hybrid feature selection algorithm work?",{"text":81,"@type":77},"It integrates several filter methods to enhance feature selection performance and reduce the number of parameters.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the framework validated and what were the results?",{"text":85,"@type":77},"Validation used a cardiovascular disease dataset, and results indicate that CVDPF combined with HFS outperforms other feature selection 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