According to the World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally, killing 17.9 million people in 2019 [1]. The WHO risk models identified many different variables as risk factors for CVDs, including the key predictor variables: age, blood pressure, body mass index, cholesterol, and tobacco use. Historically, this potpourri of factors made CVDs almost impossible to predict with any meaningful accuracy. A new study by Kondeth Fathima and E. R. Vimina [2], published in Intelligent Sustainable Systems Proceedings of ICISS 2021, used Deep Neural Networks (DNNs) with four Hidden Layers (HDs) to predict CVDs with an impressive 99% accuracy. Neural network models have come to the forefront in recent years, gaining popularity because of their exceptional prediction capabilities.