Statistical Machine Learning Algorithm for Predicting the Risk Factors in Heart Disease

Abstract

Heart disease is one of the major non-communicable disease and leading cause of mortality in the world. According to WHO heart disease is taking about nearly 17.9 million lives of people each year. Its mortality forecasts indicate a rise in global annual deaths to 20.5 million in 2020 and as high as 24.2 million by 2030. Risk factors are one of the most powerful predictors of heart disease. The study includes modified and non- modified risk factors that contribute to the disease such as Age, Gender, Family history, Hypertension, Diabetics, Obesity, Blood Pressure, Smoking, Alcohol intake, Exercise and Heart rate. Machine learning is one of the most useful techniques that can help researchers, entrepreneurs, and individuals for extracting valuable information from sets of data. The objective of this study is to highlight the utility and application of machine learning techniques for the prediction of heart disease to facilitate experts in the healthcare domain. A total of 336 patients were examined and their personal and medical data were collected in JSS hospital. This prospective study was consisting of 55% patients are free from the heart disease and 45% have heart disease. From the result, it has been determined that males are more likely to develop the heart diseases than females and very common in elderly persons. The accuracy of the Naïve Bayes model is found to be 94%, Obesity plays a vital role in getting the disease followed by hypertension, alcohol intake, smoking, exercise and age has more impact on developing the heart disease.