Neural Additive Models for Explainable Heart Attack Prediction

Abstract

Heart attack (HA) is a sudden health disorder when the flow of blood to the heart is blocked, causing damage to the heart. According to the World Health Organization (WHO), heart attack is one of the greatest causes of death and disability globally. Early recognition of the various warning signs of a HA can help reduce the severity. Different machine learning (ML) models have been developed to predict the heart attack. However, patients with arterial hypertension (AH) are especially prone to this disorder and have several features that distinguish them from other groups of patients. We apply these features to develop a special model for people suffering from AH. Moreover, we contribute to this field bringing more transparency to the modelling using interpretable machine learning. We also compare the patterns learned by methods with prior information used in heart attack scales and evaluate their efficiency.