People nowadays are engrossed in their daily routines, concentrating on their jobs and other responsibilities while ignoring their health. Because of their hurried lifestyles and disregard for their health, the number of people becoming ill grows daily. Furthermore, most of the population suffers from a disease such as cardiovascular disease. Cardiovascular disease kills 35% of the world's population, according to W.H.O. A person's life can be saved if a heart disease diagnosis is made early enough. Still, it can also be lost if the diagnosis is constructed incorrectly. Therefore, predicting heart disease will become increasingly relevant in the medical sector. The volume of data collected by the medical industry or hospitals, on the other hand, can be overwhelming at times. Time-series forecasting and processing using machine learning algorithms can help healthcare practitioners become more efficient. In this study, we discussed heart disease and its risk factors and machine learning techniques and compared various heart disease prediction algorithms. Predicting and assessing heart problems is the goal of this research
