Analyzing and Contrasting Machine Learning Algorithms for Predicting the Risk of Cardiovascular Disease

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestallment. In this study, we employ three distinct machine learning algorithms - K Nearest Neighbors(KNN), Random Forest, and Decision Tree - 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, KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacity of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings give critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention.