Toward Better Health Care Service: Statistical and Machine Learning Based Analysis of Swedish Patient Satisfaction Survey

Abstract: Patients as a customer of health care service has rights to evaluate the servicethey received, and health care providers and professionals may take advantageof these evaluations to improve the health care service. To investigate the relationshipbetween patients overall satisfaction and satisfaction of specic aspects,this study uses classical statistical and machine learning based method to analyzeSwedish national patient satisfaction survey data.Statistical method including cross tabulation, chi-square test, correlationmatrix and linear regression identies the relationship between features. It isfound that patients' demographics have a signicant association between overallsatisfaction. And patients responses in each dimension show similar trend whichwill contribute to patients overall satisfaction.Machine learning classication approaches including Nave Bayes classier,logistic regression, tree-based model (decision tree, random forest, adaptiveboosting decision tree), support vector machines and articial neural networksare used to built models to classify patients overall satisfaction (positive ornegative) based on survey responses in dimensions and patients' demographicsinformation. These models all have relatively high accuracy (87.41%{89.85%)and could help to nd the important features of health care service and henceimprove the quality of health care service in Sweden.