Improved Interpretation Model for Heart Disease Diagnosis Using Artificial Neural Networks

In the present scenario, health care is a predestined process to be considered in human life. While heart diseases are concerned, cardiovascular disease (CVD) is a wide class of diseases that damages blood vessels and the heart. In the medical field, huge health data are available to study and process; hence, machine learning methods are required for appropriate decision-making, specifically in terms of heart disease prediction and diagnosis. For enhancing the appropriation rate of decision-making in CVD diagnosis, this paper proposes an Improved Interpretation Model for Heart Disease Diagnosis (IIM-HDD) using Artificial neural networks. The model incorporates data acquisition, pre-processing, feature selection, training, and testing for diagnosis. For training and validation, the data from benchmark datasets are combined and used. Moreover, feature selection is computed with a relief-based selection process. The ANN model is trained to produce the output, corresponding to their input features. The results computations are processed with metrics that include classification, accuracy, precision rate, error rate, specificity, sensitivity, F1 score, and other comparisons are also provided for proving the proposed model. The results show that the work outperforms the other compared models in respective metrics.