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Cardiovascular diseases constitute one of the most dangerous and fatal illnesses. According to statistics, in 2019, 17.9 million deaths are reported from cardiovascular diseases. As a result, it is essential to detect the sickness early on to minimize the death rate. To handle data efficiently and precisely forecast the symptoms of illness, data mining and machine learning approaches may be applied. This study intends to employ seven supervised machine learning (ML) techniques to anticipate heart disease. The adoption of ML algorithms is the study's main objective and to investigate how feature extraction (FE) and feature selection (FS) methods might increase the effectiveness of ML models. The experimental results indicate that models with feature selection and extraction techniques outperformed the model with the entire features from the dataset. As a case study, the authors considered three additional datasets, namely Parkinson's, diabetes, and lung cancer, in addition to the Cleveland Heart Disease dataset. However, the main focus of this study is on predicting heart disease.

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