Paper Abstract and Keywords |
Presentation |
2018-06-13 15:00
A Study of Numerical Prediction of 2-hour Plasma Glucose Level during OGTT using Machine Learning Katsutoshi Maeta, Yu Nishiyama (UEC), Kazutoshi Fujibayashi (JUN), Toshiaki Gunji, Noriko Sasabe, Kimiko Iijima (NTT Medical Center Tokyo), Toshio Naito (JUN) IBISML2018-9 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Oral glucose tolerance test (OGTT) is a method used to diagnose diabetes. Subjects take a 75 g glucose solution in a short time. Subject's blood samples are collected at regular time intervals and the plasma glucose levels are measured. Plasma glucose level 2 hours after loading is used for the diagnosis of diabetes. In this paper, we apply the machine learning method XGBoost to the 75 g-OGTT data obtained from the general health checkup programs provided by the center of preventive medicine at NTT Medical Center Tokyo, and report the result of predicting OGTT 2-hour plasma glucose level from other biochemical test values. Prediction accuracy was improved using the previous value of 75 g-OGTT. However, the maximum determination coefficient obtained was 0.627. In order to improve prediction accuracy, future works include to select more adequate input variables, machine learning methods, increase the number of data, or use questionnaire data (current medical history, past history, family history, lifestyle). |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Oral Glucose Tolerance Test / OGTT / Machine Learning / XGBoost / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 81, IBISML2018-9, pp. 61-66, June 2018. |
Paper # |
IBISML2018-9 |
Date of Issue |
2018-06-06 (IBISML) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Download PDF |
IBISML2018-9 |
|