Presentation 2018-06-13
A Study of Numerical Prediction of 2-hour Plasma Glucose Level during OGTT using Machine Learning
Katsutoshi Maeta, Yu Nishiyama, Kazutoshi Fujibayashi, Toshiaki Gunji, Noriko Sasabe, Kimiko Iijima, Toshio Naito,
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Abstract(in Japanese) (See Japanese page)
Abstract(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)
Keyword(in English) Oral Glucose Tolerance Test / OGTT / Machine Learning / XGBoost
Paper # IBISML2018-9
Date of Issue 2018-06-06 (IBISML)

Conference Information
Committee NC / IBISML / IPSJ-BIO / IPSJ-MPS
Conference Date 2018/6/13(3days)
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
Chair Yutaka Hirata(Chubu Univ.) / Hisashi Kashima(Kyoto Univ.)
Vice Chair Hayaru Shouno(UEC) / Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo)
Secretary Hayaru Shouno(Nagoya Univ.) / Masashi Sugiyama(NAIST) / Koji Tsuda(Nagoya Inst. of Tech.) / (AIST)
Assistant Keiichiro Inagaki(Chubu Univ.) / Takashi Shinozaki(NICT) / Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.)

Paper Information
Registration To Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Bioinformatics and Genomics / Special Interest Group on Mathematical Modeling and Problem Solving
Language JPN
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
Keyword(5)
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)
Date 2018-06-13
Paper # IBISML2018-9
Volume (vol) vol.118
Number (no) IBISML-81
Page pp.pp.61-66(IBISML),
#Pages 6
Date of Issue 2018-06-06 (IBISML)