Rule extraction from convolutional neural networks for heart disease prediction

The accurate prediction of heart disease is crucial in the field of medicine. While convolutional neural networks have shown remarkable precision in heart disease prediction, they are often perceived as opaque models due to their complex internal workings. This paper introduces a novel method, named Extraction of Classification Rules from Convolutional Neural Network (ECRCNN), aimed at extracting rules from convolutional neural networks to enhance interpretability in heart disease prediction. The ECRCNN algorithm analyses updated kernels to derive understandable rules from convolutional neural networks, providing valuable insights into the contributing factors of heart disease. The algorithm's performance is assessed using the Statlog (Heart) dataset from the University of California, Irvine's repository. Experimental results underscore the effectiveness of the ECRCNN algorithm in predicting heart disease and extracting meaningful rules. The extracted rules can assist healthcare professionals in making precise diagnoses and formulating targeted treatment plans. In summary, the proposed method bridges the gap between the high accuracy of convolutional neural networks and the interpretability necessary for informed decision-making in heart disease prediction.