Enhancing Heart Disease Prediction: A Comparative Analysis of ML and DL Approaches

The increase in cardiovascular disease worldwide necessitates the development of effective predictive tools to facilitate early detection and intervention. The research used a variety of advanced models, including logistic regression, neural networks and random forest algorithm, focusing on the analysis of patient history data to identify eventalerts and improve accurate diagnosis. The models areselected based on their performance in processing complex data and their ability to generate meaningful content. The importance of these methods is their ability to increase the accuracy of facts and help create personalized treatment strategies in the medical center. This study also includes variousanalyses to check the result of the model on various variables and data. This paper also highlights neural networks offering potential advantages over traditional techniques in capturing complex, non-linear relationships within the data. The findings identify the most effective model for cardiovascular disease prediction and offer insights for future model optimization.