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TopAn innovative heuristic algorithm inspired by black hole phenomenon was proposed in (Hatamlou, 2013). In (Hatamlou, 2013), The black hole algorithm (BH) starts to optimize and determine a function for the original population of raw data, same as many algorithms that are based on population. Moreover, along with every iteration the best candidate is picked up to be the black hole, after which it will pull the other candidates surrounded nearby, which is known as stars. In (Mirjalili et al., 2014), Inspired by the nature of grey wolves, a new optimization method is suggested namely Grey Wolf Optimizer (GWO). This algorithm is similar to hunting grey wolves ' management hierarchy and mechanism. This algorithm is similar to the hierarchy of management and the hunting mechanism of grey wolves. To simulate their management performance, grey wolves of four distinct kinds as alpha, beta, delta and omega are introduced. Three fundamental hunting steps are introduced for accurate simulation-searching for the prey, encircling the prey, and attacking the prey. Mohan et al. 2019 proposed a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. They achieved an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM). Tarawneh & Embarak 2019 proposed a new heart disease prediction system that combine all techniques into one single algorithm, it called hybridization. The results confirm that accurate diagnose can be taken by using a combined model from all techniques. Haq et al. 2018 developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. They used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. The performance of the proposed system was validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers.