Research on Heart Disease Detection using Machine Learning and Deep Learning Techniques

Heart disease is one of the significant diseases that lead to health complications, and eventually mortality. The recognition and management of cardiac disease is intricate, particularly in developing countries, because of the limited accessibility of effective diagnostic instruments. Also, the primary elements include insufficient preventative measures and the shortage of experienced medical experts leads to severe issues. Even though a significant number of cardiac disorders are prevented, they still increase because of insufficient preventive efforts. Various researchers have built several clinical decision support systems for predicting cardiac illness to streamline and optimize diagnosis in the modern digital age. This study examines the current status of several clinical decision support systems for predicting cardiac disease, which have been developed by various researchers using machine learning (ML) and deep learning (DL) methodologies. Consequently, the development of classification models for heart disease has only achieved an intermediate level of success. Hence, there is a requirement for intricate models that integrate various data sources from different geographic locations to enhance the accuracy of disease forecasting.