콘텐츠 본문
논문 국내 국내전문학술지(KCI급) Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers
- 학술지 구분 국내전문학술지(KCI급)
- 게재년월 2010
- 저자명 Keunho Choi, Sukhoon Chung, Hyunsill Rhee, and Yongmoo Suh
- 학술지명 Healthcare Informatics Research
- 발행처명 대한의료정보학회
- 발행국가 국내
- 논문언어 외국어
- 전체저자수 4
논문 초록 (Abstract)
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 applied 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: 1) In general, the most influential variables which impact whether a patient 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. 2) The best plain classification model is dependent on the dataset. 3) 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.
