Interpretable Machine Learning-Based Algorithms for Cardiac Anomaly Detection Abstract: Cardiovascular Diseases (CDV) remain the leading cause of death globally. Accounting for an estimated 17.9 million deaths per year, according to the World Health Organization (WHO). In order to effectively treat and manage cardiac abnormalities, which lead to a number of mortality and also ultimately lower the morbidity linked to these disorders, the essential step is early detection of the disease. The main aim of this study is to look in more detail at the performance of supervised classification machine learning algorithms to detect cardiac anomalies from rest/stress myocardial perfusion imaging (MPI) in single-photon emission computed tomography (SPECT).