Classification and sequential pattern analysis for improving managerial efficiency and providing better medical service in public healthcare centers

Objectives: This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center 2) Can we anticipate diseases of patients who revisit the center Methods: For the first question, we ap-plied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results: We determined: In general, the most influential variables which impact whether a pa-tient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. The best plain classification model is dependent on the dataset. Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions: Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers.