[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121301-en":3,"doc-seo-121301-105":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},121301,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Different machine learning language models for cardiovascular disease risk prediction - a systematic review","Cardiovascular diseases remain a leading cause of mortality worldwide, creating demand for predictive tools that are both accurate and efficient. This systematic review assesses the performance of multiple machine learning algorithms for cardiovascular disease risk prediction by synthesizing studies using methods such as support vector machines, decision trees, and neural networks. Findings show consistent advantages over traditional risk assessment models for outcomes including myocardial infarction, heart failure, and stroke, with gradient boosting and deep learning achieving particularly strong accuracy. The review also discusses implementation challenges and the need for broader validation, highlighting the role of artificial intelligence in early diagnosis and risk stratification.","DOI: [https://dx.doi.org/10.18203/2320-6012.ijrms20244132](https://dx.doi.org/10.18203/2320-6012.ijrms20244132)  \nSystematic Review  \nDifferent machine learning language models for cardiovascular disease  \nrisk prediction: a systematic review  \nAlisha Lakhani1*, Abhishek Chaudhary2, Aarti Khatri3, Rahul Kantawala2, Usman Khan4, Srajan Gupta5, Tirth Bhavsar2, Ishita Vyas6, Sarayu Vejju4, Thiruvikram Sivakumar4, Aishwarya Wodeyar7, Nuha Aleemuddin8, Roshini Rai9,  \nIvaturi Sai Deepthi Janaki Rani10, Burhan Kantawala4  \n1Shantabaa Medical College, Amreli, Gujarat, India  \n2Department of Medicine, NHL Municipal Medical College, Ahmedabad, Gujarat, India 3Department of Medicine, Siberian State of Medical University, Tomsk, Russia 4Department of Medicine, Research, Vadodara, Gujarat, India  \n5Department of Medicine, S.V. Medical College Tirupati, Andhra Pradesh, India 6Medicine, Gajra Raja Medical College, Gwalior, Madhya Pradesh, India  \n7Department of Pharmacology, KS Hegde Medical Academy, Mangaluru, Karnataka, India 8Department of Medicine, Navodaya Medical College, Raichur, Karnataka, India  \n9MB BCh BAO (NUI, RCSI) LRCP&SI, Ireland  \n10Gandhi Medical College, Hyderabad, Telangana, India  \nReceived: 21 October 2024  \nRevised: 27 December 2024  \nAccepted: 28 December 2024  \n*Correspondence:  \nDr. Alisha Lakhani,  \nE-mail: [dralishalakhani@gmail.com](dralishalakhani@gmail.com)  \nCopyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nABSTRACT  \nCardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, prompting the urgent need for accurate and efficient predictive tools. This systematic review evaluates the efficacy of various machine learning algorithms in predicting cardiovascular disease risk by analyzing multiple studies that employed diverse techniques, including support vector machines, decision trees, and neural networks. The results consistently demonstrate that machine learning algorithms outperform traditional risk assessment models in predicting critical outcomes such as myocardial infarction, heart failure, and stroke, with advanced methods like gradient boosting and deep learning models showing superior accuracy. The review highlights the potential of these technologies to enhance clinical decisionmaking and improve patient outcomes, while also recognizing challenges such as implementation barriers and the need for validation across broader populations. Furthermore, the review underscores the transformative potential of machine learning in cardiovascular risk assessment, emphasizing the necessity for continued validation and adaptation to diverse patient groups. These findings affirm the growing role of artificial intelligence in revolutionizing cardiovascular care through early diagnosis and precise risk stratification, while also addressing the strengths and limitations of AI-based tools.  \nKeywords: Cardiovascular diseases, Stroke, Myocardial infarction  \nINTRODUCTION  \nCardiovascular diseases (CVDs) represent a major contributor to global mortality.1 Despite significant advancements in diagnostic procedures over the past 50  \nyears, cardiologists, primary care physicians, and other healthcare providers continue to face considerable challenges in the early detection and diagnosis of heart disease.2 Current diagnostic practices for CVD rely primarily on patient medical history, clinical assessments,  \nphysical tests, and biomarkers, which are often interpreted based on the physician's experience. This reliance on individual judgment is becoming increasingly error-prone and inefficient.3 Moreover, as cardiovascular diagnostic technologies improve and generate large volumes of data, the complexity of clinical decision-making incre","cbCaivbopSPOgl7a","https://ap.wps.com/l/cbCaivbopSPOgl7a","pdf",233815,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background: burden of cardiovascular diseases\n## Need for accurate, automated predictive tools\n## Role of AI/ML in cardiovascular risk assessment\n## Summary of prior systematic reviews and meta-analyses","[{\"question\":\"What is the main purpose of the systematic review?\",\"answer\":\"It also compares performance against traditional risk assessment approaches and discusses strengths and limitations.\"},{\"question\":\"Which clinical outcomes are highlighted in the results?\",\"answer\":\"It also emphasizes that advanced approaches like gradient boosting and deep learning can deliver superior accuracy.\"},{\"question\":\"What challenges does the review identify for applying these AI tools in practice?\",\"answer\":\"The review also notes requirements for continued validation and adaptation for diverse patient groups.\"}]","Different machine learning language models for cardiovascular disease risk prediction - 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