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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
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)
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Conference Information
Conference Date 2018-06-13 - 2018-06-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2018-06-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Numerical Prediction of 2-hour Plasma Glucose Level during OGTT using Machine Learning 
Sub Title (in English)  
Keyword(1) Oral Glucose Tolerance Test  
Keyword(2) OGTT  
Keyword(3) Machine Learning  
Keyword(4) XGBoost  
1st Author's Name Katsutoshi Maeta  
1st Author's Affiliation The University of Electro-Communication (UEC)
2nd Author's Name Yu Nishiyama  
2nd Author's Affiliation The University of Electro-Communication (UEC)
3rd Author's Name Kazutoshi Fujibayashi  
3rd Author's Affiliation Juntendo University (JUN)
4th Author's Name Toshiaki Gunji  
4th Author's Affiliation NTT Medical Center Tokyo (NTT Medical Center Tokyo)
5th Author's Name Noriko Sasabe  
5th Author's Affiliation NTT Medical Center Tokyo (NTT Medical Center Tokyo)
6th Author's Name Kimiko Iijima  
6th Author's Affiliation NTT Medical Center Tokyo (NTT Medical Center Tokyo)
7th Author's Name Toshio Naito  
7th Author's Affiliation Juntendo University (JUN)
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Speaker Author-2 
Date Time 2018-06-13 15:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2018-9 
Volume (vol) vol.118 
Number (no) no.81 
Page pp.61-66 
Date of Issue 2018-06-06 (IBISML) 

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