Chapter Eleven - Heart disease prediction with machine learning and virtual reality: from future perspective

Abstract

Maintaining health of human body is essential and complex in multifaceted ways. It consists of several organs out of which heart is counted as one of the major parts of body. It helps in pumping the blood and supplies it in the whole body. Heart disease is one of the major threats to any human being. It may cause anyone life's to death. Predicting cardiac disease is a tough task in medical science. Data analytics is the approach which is used to predict the disease from the related information. Huge amount of data has been recorded and stored by the medical centers. The collected data help in prediction of future outcomes. From the literature review, it has been observed that classification (regression, decision tree, naïve Bayes, etc.), clustering (K-means), neural networks (forward and backward propagation), and virtual reality (VR) algorithms that are used to envisage heart disease. This chapter provides sight toward existing algorithm giving overall summary of the existing work. In this study, a VR model is proposed, and three algorithms naïve Bayes, logistic regression, and random forest have been considered to predict the accuracy of prediction of heart diseases on the given data set. When it comes to predicting heart disease, the algorithm with the utmost precision is used.