머신러닝 기반 건강컨설팅 성공여부 예측모형 개발

• 영문명: Developing a Model for Predicting Success of Machine Learning based Health Consulting
• 발행기관: 한국IT서비스학회
• 저자명: 이상호(Sang Ho Lee) 송태민(Tae-Min Song)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제17권 제1호, 91~103쪽, 전체 13쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2018.02.28

영문 초록

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

목차

1. 서 론

2. 이론적 배경

3. 연구설계

4. 연구 결과

5. 결 론

키워드

Machine Learning Wellness Care Case-Based Reasoning Prediction Model Life Log Data