Incidence Rate Prediction of Diabetes from Medical Checkup Data
Summary :
To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.8 pp.1642-1646
- Publication Date
- 2017/08/01
- Publicized
- 2017/05/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016LOP0012
- Type of Manuscript
- Special Section PAPER (Special Section on Multiple-Valued Logic and VLSI Computing)
- Category
- Soft Computing
Authors
Masakazu MORIMOTO
University of Hyogo
Naotake KAMIURA
University of Hyogo
Yutaka HATA
University of Hyogo
Ichiro YAMAMOTO
Himeji Medical Association
Keyword
Latest Issue
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Masakazu MORIMOTO, Naotake KAMIURA, Yutaka HATA, Ichiro YAMAMOTO, "Incidence Rate Prediction of Diabetes from Medical Checkup Data" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 8, pp. 1642-1646, August 2017, doi: 10.1587/transinf.2016LOP0012.
Abstract: To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016LOP0012/_p
Copy
@ARTICLE{e100-d_8_1642,
author={Masakazu MORIMOTO, Naotake KAMIURA, Yutaka HATA, Ichiro YAMAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Incidence Rate Prediction of Diabetes from Medical Checkup Data},
year={2017},
volume={E100-D},
number={8},
pages={1642-1646},
abstract={To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.},
keywords={},
doi={10.1587/transinf.2016LOP0012},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Incidence Rate Prediction of Diabetes from Medical Checkup Data
T2 - IEICE TRANSACTIONS on Information
SP - 1642
EP - 1646
AU - Masakazu MORIMOTO
AU - Naotake KAMIURA
AU - Yutaka HATA
AU - Ichiro YAMAMOTO
PY - 2017
DO - 10.1587/transinf.2016LOP0012
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2017
AB - To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.
ER -
English
