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 patients' 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 are 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.