Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today's digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

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Cerebrovascular accidents (CVA) or stroke has been a global phenomenon that causes disability and deaths of people worldwide, particularly in the middle- and low-income countries. It has been reported that more than 100,000 cases are recorded every year in Nigeria. Moreover, several deaths were reported globally by the World Health Organization (WHO). Diagnostic tools, preventive measures, and medical experts are insufficient and contribute to the escalation of the disease worldwide. Several predictive models have been proposed by scholars but have been inadequate due to variability in the risk factors, race, and geographical variations. This paper compared six machine learning-based models with three feature selection algorithms on a Nigerian dataset containing 103 instances with 22 features. We trained and evaluated the NB, SVM, LR, MLP, J-48, and RF with CBFS, CAE FS, and Relief FS algorithms. The results of our experiments showed that the J-48 model with the CBFS algorithm was computationally faster and achieved an excellent prediction accuracy of 100.00% in 0.00 seconds. The type of data used has a substantial impact on the performance of machine learning classifiers. Therefore, based on the experiments performed, J-48 with CBFS algorithm was proposed for deployment as the clinical decision support system that could assist medical professionals in predicting cerebrovascular diseases.