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[한국전자통신연구원] 디지털 헬스케어 데이터 분석을 위한 머신 러닝 기술 활용 동향
테크포럼
2019-02-20 11:04:12

Ⅰ. 서론 
Ⅱ. 디지털 헬스케어 데이터 특징 
Ⅲ. 머신 러닝 데이터 전처리 및 평가 
Ⅳ. 머신 러닝 모델의 기본 유형 
Ⅴ. 머신 러닝 프레임워크 
Ⅵ. 설명 가능한 머신 러닝 분석 도구 
Ⅶ. 결론 

 

초록
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.

 

 

 

 

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