Earticle

초록

영어

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals

목차

I. 서론
 II. 진료비 예측 모형
 III. 본 연구에 사용된 데이터마이닝 기법 소개
  3.1 인공신경망(Artificial Neural Networks)
  3.2 의사결정나무(Decision Tree)
  3.3 사례기반 추론(Case-Based Reasoning)
  3.4 사례기반 추론과 인공신경망 및 의사 결정나무 기법과의 비교
 IV. 데이터 및 실험 모형
  4.1 실험 데이터
  4.2 실험 모형
 V. 실험 결과 및 해석
 VI. 결론 및 향후 연구 과제
 참고문헌

저자

• 정석훈 [ Sukhoon Chung | 고려대학교 경영대학 박사과정 ]
• 서용무 [ Yongmoo Suh | 고려대학교 경영대학 교수 ]

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