AI-Based Predictive Tools for Managing and Preventing Cardiovascular Diseases

A large percentage of deaths each year are caused by cardiovascular diseases (CVDs), which are a serious worldwide health concern. Particularly in varied groups with complicated risk profiles, the predictive value of current risk assessment techniques, such the Framingham Risk Score, is limited. In this work, we investigate how ML techniques can improve the prediction of cardiovascular risk by examining patterns that conventional models frequently overlook. Using a generated dataset designed to mimic real patient data, we applied and assessed a range of machine learning algorithms, such as logistic regression, support vector machines, random forests, XGBoost, and neural networks on a generated dataset designed to replicate actual patient information. Accuracy, sensitivity, specificity, and AUC metrics were used to evaluate each model. Our results demonstrate that ensemble approaches and neural networks perform better than traditional models, especially when it comes to identifying high-risk instances. The study takes into account how these tools could be ethically incorporated into healthcare settings in addition to their predictive power. We talk about issues with ethical use, data quality, and generalizability. All things considered, this work bolsters the expanding importance of AI in improving the efficiency, preventiveness, and personalization of cardiovascular care.