Chronic kidney disease (CKD) is a progressive malady defined by reduced glomerular filtration rate, increased urinary albumin excretion or both, and is a major global public health concern with an extremely high unmet medical need. CKD is estimated to occur in 8-16% of the worldwide population and results in a substantially reduced life expectancy. Early detection and accurate prediction of CKD is crucial to reduce health complications such as hypertension, anemia, and premature death. This study aims to develop CKD prediction models using three machine learning methods: Random Forest, Naive Bayes, and Support Vector Machine, then compare the performance of each method. The dataset used is the CKD dataset from UCI Machine Learning Repository consisting of 400 instances with 24 attributes. Experimental results show that Random Forest achieved 90.50% accuracy, Naive Bayes achieved the highest accuracy of 94.21%, while SVM achieved 88.84% accuracy. The results indicate that Naive Bayes provides the best performance for chronic kidney disease prediction with superior accuracy compared to other methods. This prediction model can assist medical practitioners in early detection and appropriate clinical decision-making for CKD patient management.
Qodri et al. (Tue,) studied this question.