Advancements in Heart Disease Detection: Integrating Machine Learning Algorithms and Clinical Insights

This study evaluates various machine learning algorithms, including deep learning, ensemble methods, and hybrid approaches, for classifying heart rhythms. It emphasizes the need for a detailed comparative analysis to understand each algorithm's strengths and weaknesses. The research highlights the importance of feature selection in enhancing predictive capabilities but notes the absence of detailed evaluation metrics such as precision, recall, F1 score, and ROC-AUC. Additionally, the study identifies gaps in understanding the significance of specific features in heart disease prediction, suggesting further investigation into feature relevance. Ethical concerns, such as data privacy, algorithmic bias, and decision-making transparency, are underscored as critical issues that need addressing to ensure the responsible and equitable deployment of predictive models in healthcare. The study advocates for a holistic approach, combining technical and ethical considerations, to advance heart disease prediction technology. Using KNN within the CDSS and a hybrid algorithm combining SVM and Random Forest, the study seeks to find the best predictive model for heart disease.