A Comprehensive Review of Data Mining and Machine Learning Techniques for Predicting Heart Disease

Abstract

: Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart diseasediagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostictools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequatepreventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors.Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures areinadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed bydifferent scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decisionsupport systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques.Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widelyemployed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in thecreation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporatemultiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.