IEICE TRANSACTIONS on Information
On Map-Based Analysis of Item Relationships in Specific Health Examination Data for Subjects Possibly Having Diabetes
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.8 pp.1625-1633
- Publication Date
- 2017/08/01
- Publicized
- 2017/05/19
- 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
Authors
Naotake KAMIURA
University of Hyogo
Shoji KOBASHI
University of Hyogo
Manabu NII
University of Hyogo
Takayuki YUMOTO
University of Hyogo
Ichiro YAMAMOTO
Himeji Medical Association
Keyword
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Naotake KAMIURA, Shoji KOBASHI, Manabu NII, Takayuki YUMOTO, Ichiro YAMAMOTO, "On Map-Based Analysis of Item Relationships in Specific Health Examination Data for Subjects Possibly Having Diabetes" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 8, pp. 1625-1633, August 2017, doi: 10.1587/transinf.2016LOP0003.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016LOP0003/_p
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@ARTICLE{e100-d_8_1625,
author={Naotake KAMIURA, Shoji KOBASHI, Manabu NII, Takayuki YUMOTO, Ichiro YAMAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={On Map-Based Analysis of Item Relationships in Specific Health Examination Data for Subjects Possibly Having Diabetes},
year={2017},
volume={E100-D},
number={8},
pages={1625-1633},
abstract={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.},
keywords={},
doi={10.1587/transinf.2016LOP0003},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - On Map-Based Analysis of Item Relationships in Specific Health Examination Data for Subjects Possibly Having Diabetes
T2 - IEICE TRANSACTIONS on Information
SP - 1625
EP - 1633
AU - Naotake KAMIURA
AU - Shoji KOBASHI
AU - Manabu NII
AU - Takayuki YUMOTO
AU - Ichiro YAMAMOTO
PY - 2017
DO - 10.1587/transinf.2016LOP0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2017
AB - 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.
ER -
English
