Review of Machine Learning Techniques for Heart Disease Prediction: Models, Challenges, and Future Directions

@article{1709104,
      author = {Sakshi Shimpi, Shreya Shingate, Zeshaan Shaikh, Prof. Sarjare. M. D., Prof. M. P. Pujari},
      title = {Review of Machine Learning Techniques for Heart Disease Prediction: Models, Challenges, and Future Directions},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {246-251},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709104.pdf},
      abstract = {Heart disease remains one of the most critical causes of global mortality. Traditional medical systems face increasing pressure due to the growing volume of patients and the limited healthcare workforce. With the advent of machine learning (ML), data-driven diagnostics have emerged as a solution to enhance early detection, patient monitoring, and risk prediction. This review presents a comprehensive evaluation of machine learning algorithms used in heart disease prediction. It highlights key challenges, examines various case studies, outlines the historical background and technological foundations, and suggests future research directions. The paper aims to bridge the gap between clinical applicability and machine learning-based innovation. Through rigorous analysis and comparison of various ML models, this review serves as a guideline for practitioners and researchers aiming to implement scalable, intelligent health diagnostic systems.},
      keywords = {Heart Disease Prediction, Machine Learning, Logistic Regression, KNN, SVM, Decision Tree, Random Forest, Healthcare AI, Clinical Diagnosis, Cardiovascular Risk, EHR Data, Early Detection, Predictive Modeling},
      month = {June},
  }