Heart disease prediction using machine learning techniques

Abstract

Heart disease is one of the most significant causes of mortality in today’s world. Heart disease proves to be the leading cause of death for both men and women. This affects the human life very badly. The diagnosis of heart disease in most cases depends on a complex combination and huge volume of clinical and pathological data. Machine learning has been shown to be effective assisting in making decisions and predictions from the large quantity of data produced by the health care industry. In this paper, various traditional machine learning algorithms that aims in improving the accuracy of heart disease prediction has been applied. In heart diseases, accurate diagnosis is primary. But, the traditional approaches are inadequate for accurate prediction and diagnosis. In order to apply deep learning technique very large datasets are required which are not available in medical and clinical research. To address this issue, surrogate data is generated from Cleveland dataset. The generated synthetic dataset is utilized with traditional machine learning algorithms as well as with deep learning model. The predicted results show that there is an improvement in classification accuracy. The generated synthetic dataset plays a vital role to improve the classification prediction particularly when dealing with sensitive data.