[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117789-en":3,"doc-seo-117789-105":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},117789,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Cardiovascular diseases prediction by machine learning incorporation with deep learning - Frontiers in Medicine research paper","Cardiovascular diseases (CVD) remain difficult to explain etiologically, while the condition is strongly linked to high mortality and severe morbidity and disability. Urgent demand exists for AI-enabled approaches that can promptly and reliably forecast future outcomes for individuals with CVD. This work leverages the Internet of Things (IoT) for data acquisition and applies machine learning to generate predictions. It addresses limitations of traditional ML models with a set of tailored ML strategies, validated by combining the Heart Dataset with additional classification models and reporting high accuracy and metric-based evaluation.","TYPE Original Research PUBLISHED 17 April 2023  \nDOI 10.3389/fmed.2023.1150933  \nOPEN ACCESS  \nEDITED BY  \nBalu Kamaraj,  \nImam Abdulrahman Bin Faisal University, Saudi Arabia  \nREVIEWED BY  \nUdhaya Kumar S.,  \nBaylor College of Medicine, United States  \nKarthick Vasudevan, Reva University, India  \n*CORRESPONDENCE  \nRohini Karunakaran  \n [rohini@aimst.edu.my](rohini@aimst.edu.my)  \nSPECIALTY SECTION  \nThis article was submitted to Precision Medicine, a section of the journal Frontiers in Medicine  \nRECEIVED 25 January 2023  \nACCEPTED 09 March 2023  \nPUBLISHED 17 April 2023  \nCITATION  \nSubramani S, Varshney N, Anand MV, Soudagar MM, Al-keridis LA, Upadhyay TK, Alshammari N, Saeed M, Subramanian K, Anbarasu K and Rohini K (2023) Cardiovascular diseases prediction by machine learning incorporation with deep learning.  \nFront. Med. 10:1150933 .  \ndoi: 10.3389/fmed.2023.1150933  \nCOPYRIGHT  \n© 2023 Subramani, Varshney, Anand, Soudagar, Al-keridis, Upadhyay, Alshammari, Saeed, Subramanian, Anbarasu and Rohini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nCardiovascular diseases prediction by machine learning incorporation with deep learning  \nSivakannan Subramani 1, Neeraj Varshney 2, M. Vijay Anand3, Manzoore Elahi M. Soudagar4, Lamya Ahmed Al-keridis 5, Tarun Kumar Upadhyay 6, Nawaf Alshammari7, Mohd Saeed7, Kumaran Subramanian 8, Krishnan Anbarasu 9 and Karunakaran Rohini 10, 11, 12*  \n1 Department of Advanced Computing, St. Joseph's University, Bengaluru, Karnataka, India, 2 Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India, 3 Department of Mechanical Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India,  \n4 Department of VLSI Microelectronics, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India, 5 Faculty of Science, Princess Norah Mint Abdulrahman University, Riyadh, Saudi Arabia,  \n6 Department of Biotechnology, Parul Institute of Applied Sciences and Centre of Research for Development, Parul University, Vadodara, India, 7 Department of Biology, College of Science, University of Hail, Hail, Saudi Arabia, 8Centre for Drug Discovery and Development, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India, 9 Department of Bioinformatics, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India, 10 Unit of Biochemistry, Centre of Excellence for Biomaterials Engeneering, Faculty of Medicine, AIMST University, Semeleing, Bedong, Malaysia, 11Centre for Excellence for Biomaterials Science AIMST University, Semeling, Bedong, Malaysia, 12 Department of Computational Biology, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India  \nIt is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanismsand training procedur","cbCaiaybIFydysPM","https://ap.wps.com/l/cbCaiaybIFydysPM","pdf",2853195,1,9,"English","en",105,"# Introduction\n## Disease burden and prediction need\n## Existing risk equations and limitations\n## Motivation for improved predictive power","[{\"question\":\"Why is cardiovascular disease prediction important?\",\"answer\":\"CVD is a leading cause of death globally and causes substantial health and socioeconomic burden. Prediction models support identifying high-risk patients and enabling prevention strategies.\"},{\"question\":\"How do IoT and machine learning contribute to the proposed approach?\",\"answer\":\"IoT devices generate data for analysis, and machine learning is used to make predictions based on that data. The study targets shortcomings of traditional ML algorithms that struggle with data differences and limited prediction accuracy.\"},{\"question\":\"How was the proposed method evaluated?\",\"answer\":\"The research validates its strategy by combining the Heart Dataset with other classification models, reporting nearly 96% accuracy and providing a complete analysis across multiple metrics.\"}]","Cardiovascular diseases prediction by machine learning incorporation with deep learning - Frontiers in Medicine research paper | PDF",1785679567,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cardiovascular-diseases-prediction-by-machine-learning-incorporation-with-deep-learning-frontiers-in-medicine-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cardiovascular-diseases-prediction-by-machine-learning-incorporation-with-deep-learning-frontiers-in-medicine-research-paper/117789/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is cardiovascular disease prediction important?","Question",{"text":75,"@type":76},"CVD is a leading cause of death globally and causes substantial health and socioeconomic burden. 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