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학술대회 Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
Cited 38 time in scopus Download 2 time Share share facebook twitter linkedin kakaostory
저자
Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, 손동구, 임기창, 박정호, 임동선, 김종면
발행일
201709
출처
International Conference on IT Convergence and Security (ICITCS) 2017 (LNEE 449), v.449, pp.35-43
DOI
https://dx.doi.org/10.1007/978-981-10-6451-7_5
협약과제
17ZS1700, 조선해양 및 육상플랜트의 스마트 HSE 시스템 개발, 장병태
초록
Accurate prediction of diabetes is an important issue in health prognostics. However, data overfitting degrades the prediction accuracy in diabetes prognosis. In this paper, a reliable prediction system for the disease of diabetes is presented using a dropout method to address the overfitting issue. In the proposed method, deep learning neural network is employed where fully connected layers are followed by dropout layers. The proposed neural network outperforms other state-of-art methods in better prediction scores for the Pima Indians Diabetes Data Set.
키워드
Data overfitting, Deep learning, Diabetes prediction, Dropout, Healthcare, Neural network
KSP 제안 키워드
Accurate prediction, Data sets, Learning neural network, Prediction System, Prediction accuracy, State-of-art, deep learning(DL), diabetes prediction