[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120359-en":3,"doc-seo-120359-105":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},120359,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Systematic Literature Review on Machine Learning in Healthcare Prediction","Rapid technological advancement is transforming healthcare, with machine learning emerging as a key driver of predictive analytics. This systematic literature review examines how machine learning is applied to healthcare prediction, and also includes limited work on machine learning operations (MLOps). Across the reviewed studies, findings indicate that appropriate datasets, well-chosen feature selection, and task-tailored algorithms can improve prediction accuracy and increase scalability and reliability. The review also highlights challenges such as specialized skills and the complexity of integrating MLOps into existing clinical systems.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 21 No. 6 (2025) |   \n[https://doi.org/10.3991/ijoe.v21i06.54211](https://doi.org/10.3991/ijoe.v21i06.54211)  \nPAPER  \nA Systematic Literature Review on Machine Learning in Healthcare Prediction  \nNur Farah Afifah Ahmad Sukri1(*), Wan Mohd Amir Fazamin Wan Hamzah1,2, Mohd Kamir Yusof1, Ismahafezi Ismail1, Harmy Mohamed Yusoff3, Azliza Yacob4  \n1Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia 2Artificial Intelligence Research Centre for Islam and Sustainability (AIRIS), Universiti Sultan Zainal Abidin, Terengganu, Malaysia  \n3Faculty of Medicine, Universiti Sultan Zainal Abidin, Terengganu, Malaysia  \n4University College TATI, Terengganu, Malaysia  \nsl4722@putra. unisza. [edu.my](edu.my)  \nABSTRACT  \nRapid technological advancement will continue to create new values and transform experiences in many sectors, including healthcare. Several key trends are shaping today’s healthcare system, including the use of machine learning (ML) . This systematic literature review (SLR) explores the application of ML in healthcare, particularly in predictive analytics. The SLR also includes a few papers on machine learning operations (MLOps) in healthcare, reflecting limited studies on the topic. This suggests significant potential for further exploration in MLOps. The review compares findings from various studies, many of which agree that ML enhances the scalability and reliability of predictive models. This study aims to assess the most effective ML algorithms and methodologies used in healthcare prediction. It also attempts to identify features influencing the outcomes of ML applications in healthcare predictions. Findings suggest that ML can improve prediction accuracy using the appropriate dataset, optimal featureselection model, and a tailored ML algorithm for specific tasks. The literature highlights challenges, including the need for specialised skills and the complexity of integrating MLOps into existing healthcare systems.  \nKEYWORDS  \nmachine learning (ML), machine learning operations (MLOps), machine learning models, machine learning algorithms, healthcare prediction, healthcare  \n1 INTRODUCTION  \nTechnological innovation has taken place in every aspect of today’s life [1] . In healthcare, both providers and patients are significantly impacted by technological advancements. Given the current global population, healthcare systems are encountering substantial challenges [2], which influenced the rise of chronic diseases and the demand for more efficient, personalised, and proactive healthcare solutions. In the old healthcare system, treatment is provided only when symptoms appear, along with general treatment plans. This approach is incompetent in today’s  \nAhmad Sukri, N.F.A., Wan Hamzah, W.M.A.F., Yusof, M.K., Ismail, I., Yusoff, H.M., Yacob, A. (2025). A Systematic Literature Review on Machine Learning in Healthcare Prediction. InternationalJournal of Online and Biomedical Engineering (iJOE), 21(6), pp. 155–177. [https://doi.org/10.3991/ijoe.v21i06.54211](https://doi.org/10.3991/ijoe.v21i06.54211)[ ](https://doi.org/10.3991/ijoe.v21i06.54211)[Article submitted 2025-01-01. Revision uploaded 2025-02-10. Final acceptance 2025-02-25.](Article submitted 2025-01-01. Revision uploaded 2025-02-10. Final acceptance 2025-02-25.)  \n© 2025 by the authors of this article. Published under CC-BY.  \niJOE | Vol. 21 No. 6 (2025) International Journal of Online and Biomedical Engineering (iJOE) 155  \nAhmad Sukri et al.  \ncomplex environment that requires more personalised care. Traditional statistical methods used to predict outcomes often struggle to consider the complex interactions among various clinical, demographic, and molecular factors impacting the progression of disease [3] .  \nTo overcome t","cbCait2z25NfGPei","https://ap.wps.com/l/cbCait2z25NfGPei","pdf",1247774,1,23,"English","en",105,"# Introduction\n## Machine learning in healthcare prediction\n## Challenges and the role of MLOps\n# Abstract and scope\n# Keywords\n# Review purpose and findings","[{\"question\":\"What is the main goal of the systematic literature review?\",\"answer\":\"To assess the most effective machine learning algorithms and methodologies used for healthcare prediction, and to identify features that influence outcomes in these applications.\"},{\"question\":\"How does the review evaluate machine learning performance in healthcare prediction?\",\"answer\":\"It compares findings across studies, focusing on how ML improves prediction accuracy through suitable datasets, optimal feature-selection models, and tailored algorithms for specific tasks.\"},{\"question\":\"What challenges does the review highlight for healthcare prediction systems and MLOps?\",\"answer\":\"Key challenges include data privacy and security, data diversity and generalisation, model personalisation, scalability and efficiency, and the complexity of integrating MLOps into existing healthcare workflows. \"}]","A Systematic Literature Review on Machine Learning in Healthcare Prediction | 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is the main goal of the systematic literature review?","Question",{"text":75,"@type":76},"To assess the most effective machine learning algorithms and methodologies used for healthcare prediction, and to identify features that influence outcomes in these applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the review evaluate machine learning performance in healthcare prediction?",{"text":80,"@type":76},"It compares findings across studies, focusing on how ML improves prediction accuracy through suitable datasets, optimal feature-selection models, and tailored algorithms for specific tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges does the review highlight for healthcare prediction systems and MLOps?",{"text":84,"@type":76},"Key challenges include data privacy and security, data diversity and generalisation, model personalisation, scalability and efficiency, and the complexity of integrating MLOps into existing healthcare 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