Comparison of decision tree and naive bayes methods in glioma classification based on clinical and molecular factors - Dewi, Ni Wayan Emmy Rosiana; Putra, I Made Suwija; Simanungkalit, Erwinsyah; Manoppo, Franky Gerald Cliford (Jurnal Mandiri IT, 2025)
This study compares the performance of Decision Tree and Naive Bayes classifiers in classifying gliomas based on clinical and molecular factors. The dataset consists of 839 patient records with features including Grade, Gender, Age, Race, and gene mutation status. The evaluation showed that the Decision Tree classifier achieved 98% accuracy on the training data and 76% on the test data, while the Naive Bayes classifier obtained 74% and 71% accuracy, respectively. Both models demonstrated strong predictive ability, with feature importance analysis highlighting the IDH1 gene mutation as a significant factor in glioma classification. This study aims to identify the most effective method for supporting clinical decision-making in glioma diagnosis. It contributes to the development of medical decision support systems and provides insight into the application of machine learning models, particularly in utilizing molecular markers such as IDH1.