Prediction of Physiotherapy Patient Satisfaction Level Using Naive Bayes Algorithm
Keywords:
Naive Bayes, Physiotherapy, Patient Satisfaction, Data Mining, ClassificationAbstract
This study aims to predict patient satisfaction levels in physiotherapy services using the Naive Bayes algorithm. Patient satisfaction is a key indicator of healthcare service quality, and this prediction is based on attributes such as age, gender, session duration, and therapist expertise. The dataset, consisting of 31 entries, was analyzed using RapidMiner software. The classification process applied the Naive Bayes model, known for its simplicity, computational efficiency, and strong performance even with limited data. Evaluation results showed an accuracy rate of 90%, with balanced precision and recall between the "satisfied" and "dissatisfied" categories. These find-ings demonstrate that data mining techniques can serve as valuable tools to support continuous improvement in physiotherapy service quality.
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Data Availability Statement
The dataset used in this study is publicly available on Kaggle and can be accessed at: https://www.kaggle.com/datasets/nurulaazizah/tingkat-kepuasanpasienterhadappelayananfisioterapi
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Copyright (c) 2025 Kaila Nazuwa Kaila (Author)
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