On Map-Based Analysis of Item Relationships in Specific Health Examination Data for Subjects Possibly Having Diabetes

Naotake KAMIURA  Shoji KOBASHI  Manabu NII  Takayuki YUMOTO  Ichiro YAMAMOTO  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.8   pp.1625-1633
Publication Date: 2017/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016LOP0003
Type of Manuscript: Special Section PAPER (Special Section on Multiple-Valued Logic and VLSI Computing)
Category: Soft Computing
specific health examination data,  lifestyle-related diseases,  self-organizing maps,  hemoglobin A1c,  

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In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (γ-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, γ-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.