Comparing Decision Tree and Support Vector Machines in Hospital Satisfaction - Anggraini, Dinda; Gamayanto, Indra; Wibowo, Sasono (Journal of Applied Informatics and Computing, 2025)
Patient satisfaction is a key indicator of hospital service quality. This study compares the performance of Decision Tree and Support Vector Machine (SVM) in classifying patient satisfaction at Harapan Hospital Magelang for service optimization. The dataset, derived from a 2024 survey, consists of 577 samples and 13 predictor variables, covering patient demographics and medical service aspects. Preprocessing includes data cleaning, normalization, encoding, and class balancing using SMOTE. The Decision Tree is applied with gini impurity and max_depth=11, while SVM uses the RBF kernel (C=100, gamma=0.01). Model evaluation metrics include accuracy, precision, recall, F1-score, and ROC-AUC.Results show that Decision Tree outperforms SVM, achieving 86% accuracy vs. 81%. It also has 86% precision and 95% recall for the Dissatisfied category, higher than SVM (93% recall). The McNemar test confirms a statistically significant performance difference (p-value = 0.037). With higher accuracy and interpretability, Decision Tree is recommended as the primary method for hospital patient satisfaction analysis. These findings support the development of an adaptive classification system for Indonesian healthcare data.