Alzheimer-type dementia prediction by sparse logistic regression using claim data
Copyright © 2020. Published by Elsevier B.V..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:196 |
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| Enthalten in: |
Computer methods and programs in biomedicine - 196(2020) vom: 01. Nov., Seite 105582 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fukunishi, Hiroaki [VerfasserIn] |
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| Links: |
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| Themen: |
Alzheimer-type dementia |
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| Anmerkungen: |
Date Completed 14.05.2021 Date Revised 14.05.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.cmpb.2020.105582 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM312785895 |
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| 520 | |a Copyright © 2020. Published by Elsevier B.V. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Alzheimer-type dementia | |
| 650 | 4 | |a Health insurance claim data | |
| 650 | 4 | |a Long-term care insurance claim data | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Prediction | |
| 650 | 4 | |a Sparse logistic regression | |
| 700 | 1 | |a Nishiyama, Mitsuki |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Kubo, Masahiro |e verfasserin |4 aut | |
| 700 | 1 | |a Kobayashi, Yasuki |e verfasserin |4 aut | |
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