Exploring Predictive Methods for Cardiovascular Disease: A Survey of Methods and Applications

Abstract

Because cardiovascular disease (CVD) is still one of the world’s leading causes of death, sophisticated predictive models are required for early detection and prevention. This study examined how to make and compare different CVD prediction models using a large dataset that included biochemical, clinical, and demographic information about each person. During the preprocessing stage, we took great care to ensure the data’s accuracy and quality. We have utilized a variety of machine learning algorithms such as random forest, logistic regression, support vector machines, and deep learning neural networks. We assessed the performance of these models using the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our findings show that while more sophisticated algorithms—especially deep learning models—perform better at spotting possible instances of CVD, more conventional models—such as regression—offer significant predictive power. We also investigated the role that feature selection has in improving the interpretability and efficiency of the model. This study highlights the potential of machine learning to transform CVD prediction and emphasizes the importance of using many forms of data to provide a thorough risk evaluation. Our research adds to the continuing efforts in personalized medicine by providing information on creating more precise and effective predictive tools for cardiovascular health management.