BROWSE

Related Scientist

Lee, Woo Seok's photo.

Lee, Woo Seok
복잡계 이론물리 연구단
more info

ITEM VIEW & DOWNLOAD

Machine learning for the diagnosis of early-stage diabetes using temporal glucose profiles

Cited 0 time in webofscience Cited 0 time in scopus
6 Viewed 0 Downloaded
Title
Machine learning for the diagnosis of early-stage diabetes using temporal glucose profiles
Author(s)
Woo Seok Lee; Junghyo Jo; Taegeun Song
Publication Date
2021-03
Journal
JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.78, no.5, pp.373 - 378
Publisher
KOREAN PHYSICAL SOC
Abstract
Machine learning shows remarkable success for recognizing patterns in data. Here, we apply machine learning (ML) for the diagnosis of early-stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of glucose homeostasis leads to a common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period that complicates detection of the disease at an early stage. The vast majority of diabetes cases result from that diminished effectiveness of insulin action, and that insulin resistance modifies the temporal profile of blood glucose. Thus, we propose to use ML to detect subtle changes in the temporal pattern of the glucose concentration. Time series data on blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal by using synthetic glucose profiles produced using a biophysical model that considers glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above 85%.
URI
https://pr.ibs.re.kr/handle/8788114/9907
DOI
10.1007/s40042-021-00056-8
ISSN
0374-4884
Appears in Collections:
Center for Theoretical Physics of Complex Systems(복잡계 이론물리 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse