Intelligent Cardiovascular Disease Risk Estimation Prediction System

Cardiovascular diseases have been on the rise from the past few decades. In developing countries, cardiovascular diseases (CVDs) have become one of the most causes of death around the world. Thus, there is a need to predict CVDs at the earlier stages to minimize treatment costs and avoid critical cases. Thus, this work provides a novel method to predict cardiovascular diseases using machine learning. A comparison between various machine learning algorithms is made to analyse the performance on the dataset and the proposed method uses s K Nearest Neighbour which has an accuracy of 92.30%. Additionally, data reduction techniques have been utilised to reduce the number of attributes to 11 which significantly outperforms the related work.