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

Keunho Choi, Sukhoon Chung, Hyunsill Rhee, Yongmoo Suh

Research output: Contribution to journalArticle

10 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)67-76
Number of pages10
JournalHealthcare Informatics Research
Volume16
Issue number2
DOIs
Publication statusPublished - 2010

Keywords

  • Classification analysis
  • Data mining
  • Ensemble method
  • Public healthcare center
  • Sequential pattern analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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