Authors:
Keisuke Ogawa
1
;
Kazunori Matsumoto
1
;
Masayuki Hashimoto
1
and
Ryoichi Nagatomi
2
Affiliations:
1
KDDI R&D Labs, Japan
;
2
Tohoku Graduate School of Biomedical Engineering, Japan
Keyword(s):
Latent Dirichlet Allocation, LDA, Metabolic Syndrome, Lifestyle-Related Disease.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cloud Computing
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
e-Health
;
Enterprise Information Systems
;
Health Information Systems
;
Pattern Recognition and Machine Learning
;
Platforms and Applications
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Recently, the number of patients with lifestyle-related diseases, such as diabetes mellitus, has increased
dramatically. Lifestyle-related diseases are responsible for 60% of deaths in Japan. In order to screen
persons at potentially high risk for these diseases, medical checkups for metabolic syndrome are used
throughout Japan. Prediction and prevention of lifestyle-related diseases would yield a direct reduction in
medical costs. However, many cases cannot be screened with a metabolic syndrome checkup. In this paper,
we propose a new machine-learning-based screening method using medical checkup data and medical
billings. By processing the medical data into a bag-of-words representation and classifying the health factors
using latent Dirichlet allocation (LDA), the screening method achieves high accuracy. We evaluate the
method by comparing the accuracy of predictions of the future incidence of the diseases. The results show
that F-measure increases 0.17 compared with the convention
al method. In addition, we confirmed that the
proposed method classified persons with different health risk factors, such as a combination of metabolic
disorders, hypertensive disorders, and mental disorders (stress).
(More)