Unveiling the Potential of Machine Learning: Harnessing Machine Learning for Enhanced Coronary Heart Disease Detection and Intervention

Coronary heart disease (CHD) remains a critical global health issue, necessitating effective predictive models to enhance early diagnosis and intervention. This study explores the application of various machine learning algorithms to CHD data to identify and predict the risk of developing the disease based on health indicators. We employed several algorithms, including Logistic Regression, Random Forest Classifier, Support Vector Machine (SVM), and Recurrent Neural Network (RNN), each with its unique strengths and limitations. The Logistic Regression model achieved an accuracy of approximately 86%, proving effective in binary classification tasks but revealing potential class imbalance issues. The Random Forest Classifier demonstrated robustness with an accuracy of around 85.24%, adeptly handling complex datasets. While specific accuracy metrics for the SVM were not detailed, it was noted for its classification capabilities. The RNN achieved an accuracy of approximately 85.96%, effectively capturing temporal dependencies within the data. Results indicate that although all models performed well, challenges such as class imbalance impacted performance metrics, especially in predicting CHD cases. This highlights the need for further refinement in predictive models to improve precision, recall, and F1-scores. Later research will examine the integration of advanced deep learning methodologies to enhance the precision and dependability of CHD forecasts.