Performance Comparison of Decision Tree J48, CART, and Naïve Bayes Algorithms for Predicting Chronic Kidney Disease - Ikhsan, Ali Nur; Fadilah, Alif Nur; Iftinani, Alifah Dafa (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2024)
Chronic Kidney Disease could be a worldwide issue that proceeds to extend with high treatment costs. Accurate diagnosis is essential for managing this disease. There is a requirement for a technique to anticipate chronic kidney disease, with prevalent use being made of Decision Tree J48, Naive Bayes, and CART algorithms which offer benefits like swift computation, ease of use, and high precision. The researchers aimed to determine the comparison results of Decision Tree J48, CART, and Naive Bayes algorithms for predicting chronic kidney disease. From the research findings, it was concluded that the CART algorithm had the highest accuracy rate of 97.25% in predicting chronic kidney disease, compared to the J48 Decision Tree algorithm and the Naïve Bayes algorithm with accuracy rates of 96.5% and 93.5% respectively. The CART algorithm can be utilized by pathologists to develop a program for predicting chronic kidney disease.