Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning

Haein Lee, Seon Lee, Dongyan Nan, Jang Kim

OAI: oai:igi-global.com:300766 • DOI: 10.4018/JOEUC.300766

Outbreak of the COVID-19 leads to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that Logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with Bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and Logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.

End-User Computing Social Sciences and Humanities Human Aspects of Technology