A SURVEY OF PERFORMANCES OF SOME SELECTED MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION - YAHAYA, LAMIDO; HASSAN, IBRAHIM; RABIU, ABBAS MUHAMMAD (BIMA JOURNAL OF SCIENCE AND TECHNOLOGY (2536-6041), 2020)
Cardio-Vascular Diseases (CVDs) are the leading causes of early deaths in the world. Middleand low-income countries suffer the biggest challenge of effective diagnosis and treatment due to the inadequacy of efficient diagnostic tools and physicians. This affects the proper prediction and treatment of patients. Though, large proportion of CVDs could be prevented but they continue to escalate mainly because preventive measures put in place are inadequate. Huge CVD data is available in the healthcare sector which led to several researches. The University of California, Irvine (UCI) heart disease data has been used extensively by machine learning researchers in trying to come up with a more efficient predictive model. Previously, the focus was on investigating the performances of some selected machine learning algorithms on the UCI data. These algorithms include Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT-J48), Random Forest (RF), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). There are few researchers who used CVD datasets other than that of the UCI. This paper carries out a survey on the performances of these algorithms on CVD prediction using 12various datasets other than the UCI data. From our investigation on the 18 researches conducted, most of them in 2018 and 2019, we found that DT-J48, NB and SVM gained much attention than any other algorithm, where J48 was used 11 times and appeared the most used algorithm for developing clinical decision support systems. NB and SVM appeared 10 and 9 times respectively, ANN was employed 8 times, while KNN and LR were considered 3 times each. RF appeared with the least frequency of 1 only. Finally, it has been discovered that no single algorithm would be generalized as the best in CVD prediction based on the data in which it was used.