A Clinical Decision Support System for Heart Disease Prediction Using Deep Learning

Abstract

The major cause of death globally is heart disease and it is increasing day by day. It is difficult to diagnose the heart disease at early stages until a cardiac issue occurs. Huge amount of heart disease data is available in the health care sector such as in clinics, hospitals etc. However, this data is not intelligently handled to extract useful knowledge. Machine learning techniques help in turning this medical data into useful knowledge. Machine learning is used to design such decision support systems (DSS) that can learn and improve from their past experiences. Recently, deep learning has gained the interest of industry and academics. This research paper focuses on diagnosis of heart disease with high accuracy. In the proposed approach, Keras based deep learning model is used for the computation of results using dense neural network. The proposed model is tested with different combination of hidden layers in dense neural network, started from 3layers to 9layers. Each hidden layer uses 100 neurons and activation function as Relu. Multiple benchmark heart disease datasets have been used for the analysis. Individual as well as Ensemble models are evaluated on all heart disease datasets. Moreover, dense neural network is also evaluated on all datasets using accuracy, sensitivity, specificity and f-measure. Different combination of layers performed well on different datasets due to different categories of attributes. This extensive experimentation is performed to show the effectiveness of proposed model. The analysis of results shows that proposed model based on deep learning performed significantly better in terms of accuracy, sensitivity and specificity as compared to individual and other ensemble techniques on all heart disease datasets.