3066-280XISSN (Online)
Volume 2 , Issue 2 , PP: 26-36, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Safa S. Abdul-Jabbar 1 *
Doi: https://doi.org/10.54216/MOR.020203
Heart diseases go on to be the primary cause of such mortality all over the world and hence call for accurate and efficient diagnostic tools. Traditional diagnostics are not scalable and precise in analyzing large and complex datasets generated in healthcare. Machine learning has come as a revolutionary solution in the form of advanced prediction models in the diagnosis and risk assessment of heart diseases. The authors present all machine-learning techniques like Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, and hybrid models containing deep learning versions like CNN and LSTM in the study. These techniques consumed multi-source data found in Cleveland, Statlog, and UCI repositories and combined feature selection methods with different data preprocessing techniques to achieve improved accuracy, reliability, and scalability of outcomes while applying ensemble methods like majority voting and boosting to show enhancements in model working robustness and adopting SMOTE to tackle the imbalanced data scenario. Despite these developments, specific challenges remain mostly: Model Interpretability, Data Diversity, and Clinical Integration. The present review discusses progress, challenges, and future avenues in using machine learning in predicting heart diseases, which focus on the critical need for explainable AI models, diverse datasets, and real-world validation for the optimum use of clinical applications to improve global healthcare outcomes eventually.
Machine learning , Heart disease prediction , Random Forest , Feature selection , Hybrid models , explainable AI
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