Prediction of the Effect of Specialist Services on Patient Satisfaction using the SVM Method

Mehma Preet Kaur, Ermi Girsang, Sri Lestari

Abstract

The availability of specialist doctors is absolutely necessary for curative services in hospitals because professional services are at the core of patient satisfaction. The existence of patient complaints about the services of specialist doctors has an impact on patients dissatisfaction. The purpose of this study is to develop a model that is able to predict the effect of the quality of specialist services on patient satisfaction based on previous services. For development and testing, patients with a population of 750 respondents with 88 samples were used. Modeling was built using the support vector machine method. For weighting the model, the study data using univariate, bivariate with chi-square test, and multivariate with multiple logistic regression at a 95% confidence level (α = 0.05) were used. The results showed that the accuracy of the built model by 91.7% was achieved, where there was an effect of reliability, responsiveness, and assurance on inpatient satisfaction p <0.05. While the tangible and empathy variables have no significant effect. The variable that had the greatest influence on patient satisfaction was assurance with a 9.5 times higher chance of poor specialist medical guarantees.

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Paper Citation


in Harvard Style

Kaur M., Girsang E. and Lestari S. (2020). Prediction of the Effect of Specialist Services on Patient Satisfaction using the SVM Method.In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP, ISBN 978-989-758-500-5, pages 141-148. DOI: 10.5220/0010291601410148


in Bibtex Style

@conference{himbep20,
author={Mehma Preet Kaur and Ermi Girsang and Sri Lestari},
title={Prediction of the Effect of Specialist Services on Patient Satisfaction using the SVM Method},
booktitle={Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP,},
year={2020},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010291601410148},
isbn={978-989-758-500-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP,
TI - Prediction of the Effect of Specialist Services on Patient Satisfaction using the SVM Method
SN - 978-989-758-500-5
AU - Kaur M.
AU - Girsang E.
AU - Lestari S.
PY - 2020
SP - 141
EP - 148
DO - 10.5220/0010291601410148