Predicting the onset of Cardiovascular Diseases using Machine Learning Techniques

Abstract

Cardiovascular diseases are one of the major diseases that consume many lives each year. The early prediction of the disease can help save a lot of lives, thus arising the need for better intelligent systems. Data mining is one of the popular fields in Computer sciences that mines an enormous amount of data to generate knowledge. Researchers are using these data mining and machine learning techniques to analyse the data related to health and predict the probability of the onset of any disease. In this research article, we have proposed a model trained using supervised machine learning algorithms to predict the onset of the cardiovascular disease. The various algorithms that were used to train the machine learning model are Sequential Minimal Optimization, Decision tress, Naïve Bayes and Random Forest. These algorithms were implemented using WEKA tool. The best performing algorithm was SMO that attained an overall accuracy of 83.4 % in predicting this deadly disease.