CardioSage: A Machine Learning-Based Cardiovascular Disease Prediction System

Cardiovascular diseases have emerged as a significant global health concern, affecting a large portion of the population. Early detection and accurate prediction of such diseases are crucial for providing dedicated care and minimizing the associated risks. To address this need, researchers have turned to machine learning algorithms and tools to analyze vast and complex datasets in the medical domain. In recent years, there has been a surge in the utilization of various machine learning techniques to assist in the intricate task of identifying heart-related issues.This study aims to explore and evaluate different models based on machine learning algorithms, namely Random Forest, KNN, Extra tree classifier, and XG boost, for the prediction of cardiovascular diseases. These models leverage the power of machine learning to analyze intricate patterns within the datasets, enabling the identification of potential risk factors and accurate disease prediction.