App Based Cardiovascular Disease Prediction using Machine Learning Algorithms

Study investigates machine learning (ML) algorithms for early detection of cardiovascular diseases (CVDs), given their significant global impact. Utilizing demographic data, medical history, and lab results, algorithms like Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors are examined. An ensemble model, incorporating Random Forest, is developed for improved accuracy, initially achieving 95%. Hyperparameter optimization boosts accuracy to 99%. An Android app interface facilitates easy access to the predictive model, empowering users for CVD risk assessment. These findings underscore ML' s potential in early CVD detection, offering advancements in predictive healthcare. This contributes to addressing CVD burdens and improving public health outcomes.