Alzheimer-type dementia prediction by sparse logistic regression using claim data.
Alzheimer-type dementia
Health insurance claim data
Long-term care insurance claim data
Machine learning
Prediction
Sparse logistic regression
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
06
11
2019
accepted:
30
05
2020
pubmed:
24
7
2020
medline:
15
5
2021
entrez:
24
7
2020
Statut:
ppublish
Résumé
This study aimed to predict the risk of Alzheimer-type dementia for persons aged over 75 years old without receiving long-term care services using regularly collected claim data. A refined dataset including 48,123 persons was prepared from claim data of health insurance and long-term care insurance in a large city in the metropolitan area in Japan. The utilized features include the age and sex of subjects, 502 diseases based on ICD-10 diagnosis codes, and 107 prescription drugs based on therapeutic classes. The most important challenge in this work was feature selection form a large number of features. We adopted sparse logistic regression models with L0 regularization (SLR-L0) and L1 regularization (SLR-L1) as classification models based on machine learning. These regularizations enable feature selection by estimating sparse solution of non-zero coefficients in the model optimization. Predictions were performed by integrating 100 predictors trained by bootstrap samples. As a result, the area under the ROC curves (AUCs) were 0.663 for SLR-L0 and 0.660 for SLR-L1. These performances were similar, however, the average numbers of selected features were 13 out of a total of 611 for SLR-L0 and 253 for SLR-R1. The results indicate that SLR-L1 tended to include less useful features, whereas SLR-L0 narrowed down influential features. SLR-L0 might be more useful than SLR-L1 for practical use or the discussion of risk factors with medical experts.
Identifiants
pubmed: 32702573
pii: S0169-2607(20)31415-2
doi: 10.1016/j.cmpb.2020.105582
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105582Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest H. Fukunishi was a researcher at the Data Science Research Laboratories, NEC Corporation, Japan, from the beginning of this study to August 2017. Y. Kobayashi received the research fund based on a collaborative research project conducted by The University of Tokyo and NEC Corporation to investigate a method of preventing diseases. The authors declare no conflict of interest.