A Comprehensive Review of Machine Learning for Heart Disease Prediction: Challenges, Trends, Ethical Considerations, and Future Directions

Raman Kumar¹Sarvesh Garg²Rupinder Kaur³Johar MGM⁴Sehijpal Singh¹Soumya V Menon⁵Pulkit Kumar⁶Ali Mohammed Hadi⁷Shams Abbass Hasson⁸Jasmina Lozanovic^9*

• ¹Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, Ludhiana, Punjab, India
• ²Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India, Ludhiana, India
• ³Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India, Ludhiana, India
• ⁴Management and Science University, Shah Alam, Selangor, Malaysia, Selangor, Malaysia
• ⁵Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India, Karnataka, India
• ⁶Center for Research Impact and Outcome, Chitkara University, Chandigarh, Punjab, India
• ⁷Mazaya University College, Dhi-Qar, Iraq
• ⁸Laboratories Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, 51001, Babylon, Iraq, Babylon, Iraq
• ⁹University of Applied Sciences Wien, Vienna, Austria

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.