Comparative Analysis of Machine Learning Algorithms for Cardiovascular Disease Risk Prediction

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestalment. In this study, we employ four distinct machine learning algorithms — K Nearest Neighbors (KNN), Random Forest, Decision Tree and Support Vector Machine — to prognosticate the threat of cardiovascular conditions. The dataset used in this exploration consists of 14 essential features and has been sourced from Kaggle, a prominent platform for data wisdom competitions. Our primary idea is to estimate the prophetic performance of these algorithms and determine which algorithm gives us maximum accuracy. Among the algorithms examined, the KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacy of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings provide critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention.