Publication

Abstract : Heart disease is currently one of the leading causes of death worldwide. Predicting cardiac diseases is a significant challenge in clinical data analysis. Machine learning is useful for generating judgments and predictions based on the significant amount of data generated by healthcare businesses. Using patient heart disease data, this research presents a heart disease prediction module using the grey wolf optimization (GWO)-tuned artificial neural network (ANN) classifier. The effectiveness of the proposed module relies on the optimal tuning of the weights of the ANN classifier using the GWO algorithm, which involves the hierarchy-based prey-hunting features of grey wolves. This enhances the prediction accuracy of the proposed model. The efficiency of the proposed model was analyzed in terms of performance indices such as accuracy, sensitivity, specificity, and F1-score, which were determined to be 92.9245%, 94.7469%, 90.9215%, and 96.0551%, respectively. The experimental results obtained using the proposed GWO-ANN module validated the efficiency of the proposed strategy compared with conventional models.