Innovative Machine Learning Approaches to Cardiovascular Disease Prediction

Cardiovascular diseases (CVDs) are a principal factor in health issues and death throughout the world, introducing extensive challenges to healthcare systems and impacting countless lives. This paper explores the application of various machine learning models to predict and monitor CVDs, leveraging data from wearable sensors and smart health devices. We analysed models featuring Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting, KNearest Neighbors, and Voting Classifiers. Our in-depth examination revealed that most models achieved remarkable accuracy, precision, recall, and F1-scores with the Voting Classifier. These models’ high accuracy and straightforward implementation make them suitable for real-world clinical settings, where computational resources may be limited. By enabling early detection and continuous monitoring, these machine learning approaches can personalize healthcare, improve patient outcomes, and reduce healthcare costs. The integration of these advanced technologies promises to enhance cardiovascular disease management, providing timely interventions and better health monitoring, ultimately contributing to reduced incidence and severity of CVDs.