Heart disease is one of the most important problems the world faces. It is an ongo-ing problem and it is leading to the cause of death globally. To solve this issue, predicting early heart disease is important. This research focuses on supervised machine learning techniques as a potential tool for heart disease prediction. This study has done a comprehensive review of 30 articles published between 1997 to 2023 about machine learning techniques to predict heart disease. The common problem is authors use different data sets, and different numbers of parameters to train and test these models. These two factors could affect the model's accuracy. To compare different models, I only used articles that analyze more than one method using the same data to prevent bias. Some traditional machine learning methods such as Artificial Neural Network, and K-Nearest Neighbor demonstrated significant variation in accuracy, occasionally reaching as high as 100% but sometimes falling below 60% in specific situations which is inconsistent. Compared to these models, Hybrid Models show consistent accuracy, with a minimum accuracy rate of 88%, suggesting that they could be a better approach to predicting heart disease.
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