Questionnaire Based Hospital Patient Satisfaction Level Classification With Support Vector Machine

Abstract

The utilization of machine learning in various questionnaire-based classifications, especially using the Support Vector Machine (SVM) algorithm, has piqued our interest in conducting research on hospital patient satisfaction levels through a survey. Using nine questions as features and measuring the patient's willingness to recommend RS Haji Medan to others, we built three classification models with Polynomial, RBF, and Sigmoid kernel functions. Out of the 86 responses we received, our t-test validation test revealed that all the questions we asked were valid for use in the classification process. The results show that the Polynomial model produced the highest accuracy (90.5%), precision (91.8%), and recall (90.5%) when compared to the RBF and Sigmoid models. Furthermore, the generated model exhibits stable performance, with an average difference of less than 7% between the training and testing performance. This stability suggests promising resistance to overfitting and underfitting.