Predicting Patients' Satisfaction With Doctors in Online Medical Communities: An Approach Based on XGBoost Algorithm

Yunhong Xu, Guangyu Wu, Yu Chen

Source Title: Journal of Organizational and End User Computing (JOEUC)34(4)

ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893271|DOI: 10.4018/JOEUC.287571

MLA

Xu, Yunhong, et al. "Predicting Patients' Satisfaction With Doctors in Online Medical Communities: An Approach Based on XGBoost Algorithm." JOEUC vol.34, no.4 2022: pp.1-17. http://doi.org/10.4018/JOEUC.287571

APA

Xu, Y., Wu, G., & Chen, Y. (2022). Predicting Patients' Satisfaction With Doctors in Online Medical Communities: An Approach Based on XGBoost Algorithm. Journal of Organizational and End User Computing (JOEUC), 34(4), 1-17. http://doi.org/10.4018/JOEUC.287571

Chicago

Xu, Yunhong, Guangyu Wu, and Yu Chen. "Predicting Patients' Satisfaction With Doctors in Online Medical Communities: An Approach Based on XGBoost Algorithm," Journal of Organizational and End User Computing (JOEUC) 34, no.4: 1-17. http://doi.org/10.4018/JOEUC.287571

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Abstract

Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and predicting their satisfaction can help patients narrow down their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, we integrated XGBoost and SMOTE algorithm to examine what factors and these factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithm can achieve better performance. We further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.