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http://dx.doi.org/10.9723/jksiis.2020.25.6.033

Deep Learning Algorithm and Prediction Model Associated with Data Transmission of User-Participating Wearable Devices  

Lee, Hyunsik (CHA Univ. Dept. of Integrated Medicine)
Lee, Woongjae (Seoul Women's Univ. Dept. of Digital Media)
Jeong, Taikyeong (Sehan Univ. Dept. of Artificial Intelligence)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.25, no.6, 2020 , pp. 33-45 More about this Journal
Abstract
This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.
Keywords
Deep learning; Wearable device; Digital healthcare; Disease prediction; Genome; Life-log;
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