A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions
This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1583459/full |
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| author | Raman Kumar Raman Kumar Sarvesh Garg Rupinder Kaur M. G. M. Johar Sehijpal Singh Sehijpal Singh Soumya V. Menon Pulkit Kumar Pulkit Kumar Ali Mohammed Hadi Shams Abbass Hasson Jasmina Lozanović |
| author_facet | Raman Kumar Raman Kumar Sarvesh Garg Rupinder Kaur M. G. M. Johar Sehijpal Singh Sehijpal Singh Soumya V. Menon Pulkit Kumar Pulkit Kumar Ali Mohammed Hadi Shams Abbass Hasson Jasmina Lozanović |
| author_sort | Raman Kumar |
| collection | DOAJ |
| description | This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for early and precise diagnostic tools. ML models hold considerable potential by utilizing large-scale healthcare data to enhance predictive diagnostics. To systematically investigate this field, the literature is organized into five thematic categories such as “Heart Disease Detection and Diagnostics,” “Machine Learning Models and Algorithms for Healthcare,” “Feature Engineering and Optimization Techniques,” “Emerging Technologies in Healthcare,” and “Applications of AI Across Diseases and Conditions.” The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). Several real-world case studies are presented to demonstrate the successful deployment of ML models in clinical and wearable settings. This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. The conclusions provide important insights for the development of artificial intelligence (AI) powered, clinically applicable heart disease prediction systems. |
| format | Article |
| id | doaj-art-990218cf23034e1d9dd8ff491ac06ae9 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-990218cf23034e1d9dd8ff491ac06ae92025-08-20T02:59:12ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-05-01810.3389/frai.2025.15834591583459A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directionsRaman Kumar0Raman Kumar1Sarvesh Garg2Rupinder Kaur3M. G. M. Johar4Sehijpal Singh5Sehijpal Singh6Soumya V. Menon7Pulkit Kumar8Pulkit Kumar9Ali Mohammed Hadi10Shams Abbass Hasson11Jasmina Lozanović12Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, IndiaJadara Research Center, Jadara University, Irbid, JordanDepartment of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, IndiaDepartment of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, IndiaManagement and Science University, Shah Alam, MalaysiaDepartment of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, IndiaDepartment of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun, IndiaDepartment of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, IndiaDepartment of Electrical Engineering, Chandigarh University, Mohali, IndiaChitkara University Institute of Engineering and Technology, Centre for Research Impact & Outcome, Chitkara University, Rajpura, India0Department of Pharmacy, Mazaya University College, Dhiqar, Iraq1Laboratories Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, Babylon, Iraq2Department of Engineering, FH Campus Wien - University of Applied Sciences, Vienna, AustriaThis review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for early and precise diagnostic tools. ML models hold considerable potential by utilizing large-scale healthcare data to enhance predictive diagnostics. To systematically investigate this field, the literature is organized into five thematic categories such as “Heart Disease Detection and Diagnostics,” “Machine Learning Models and Algorithms for Healthcare,” “Feature Engineering and Optimization Techniques,” “Emerging Technologies in Healthcare,” and “Applications of AI Across Diseases and Conditions.” The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). Several real-world case studies are presented to demonstrate the successful deployment of ML models in clinical and wearable settings. This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. The conclusions provide important insights for the development of artificial intelligence (AI) powered, clinically applicable heart disease prediction systems.https://www.frontiersin.org/articles/10.3389/frai.2025.1583459/fullheart disease predictionmachine learning (ML)deep learning modelsfederated learningexplainable artificial intelligence (XAI) |
| spellingShingle | Raman Kumar Raman Kumar Sarvesh Garg Rupinder Kaur M. G. M. Johar Sehijpal Singh Sehijpal Singh Soumya V. Menon Pulkit Kumar Pulkit Kumar Ali Mohammed Hadi Shams Abbass Hasson Jasmina Lozanović A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions Frontiers in Artificial Intelligence heart disease prediction machine learning (ML) deep learning models federated learning explainable artificial intelligence (XAI) |
| title | A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions |
| title_full | A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions |
| title_fullStr | A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions |
| title_full_unstemmed | A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions |
| title_short | A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions |
| title_sort | comprehensive review of machine learning for heart disease prediction challenges trends ethical considerations and future directions |
| topic | heart disease prediction machine learning (ML) deep learning models federated learning explainable artificial intelligence (XAI) |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1583459/full |
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