Incidence Rate Prediction of Diabetes from Medical Checkup Data

Masakazu MORIMOTO  Naotake KAMIURA  Yutaka HATA  Ichiro YAMAMOTO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.8   pp.1642-1646
Publication Date: 2017/08/01
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
Keyword: 
specific health examination,  lifestyle-related disease,  machine learning,  

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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.