Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Trend of Utilization of Machine Learning Technology for Digital Healthcare Data Analysis

디지털 헬스케어 데이터 분석을 위한 머신 러닝 기술 활용 동향

  • Published : 2019.02.01
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Abstract

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

Keywords

HJTOCM_2019_v34n1_98_f0001.png 이미지

(그림 1) 디지털 스마트 헬스케어 패러다임

HJTOCM_2019_v34n1_98_f0002.png 이미지

(그림 2) 머신 러닝 분석 흐름

HJTOCM_2019_v34n1_98_f0003.png 이미지

(그림 3) (a) Breast cancer 데이터 속성과 (b) 가시화

HJTOCM_2019_v34n1_98_f0004.png 이미지

(그림 4) (a)분류 행렬과 (b)유방암 데이터의 분석(XGBoost적용) 사례

HJTOCM_2019_v34n1_98_f0005.png 이미지

(그림 5) 유방암 데이터의 모델 평가 지표

HJTOCM_2019_v34n1_98_f0006.png 이미지

(그림 6) 유방암 데이터의 모델 평가 결과의 ROC 곡선

HJTOCM_2019_v34n1_98_f0007.png 이미지

(그림 7) Breast Cancer 데이터[12]의 XGBoost 적용 분석 및 피쳐 중요도 ([29]로 구성)

HJTOCM_2019_v34n1_98_f0008.png 이미지

(그림 8) 유방암 데이터의 XGBoost분석에 대하여SHAP에 의한 예측요인 설명([33]의 재구성)

<표 1> 데이터 가시화 도구

HJTOCM_2019_v34n1_98_t0001.png 이미지

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