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Alzheimer-type dementia prediction by sparse logistic regression using claim data.

Hiroaki Fukunishi1, Mitsuki Nishiyama2, Yuan Luo3

  • 1School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji City, Japan.

Computer Methods and Programs in Biomedicine
|July 24, 2020
PubMed
Summary
This summary is machine-generated.

This study predicts Alzheimer-type dementia risk in older adults using health insurance data. Sparse logistic regression with L0 regularization identified key risk factors more effectively than L1 regularization.

Keywords:
Alzheimer-type dementiaHealth insurance claim dataLong-term care insurance claim dataMachine learningPredictionSparse logistic regression

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Area of Science:

  • Gerontology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Alzheimer-type dementia poses a significant public health challenge, particularly for the elderly population.
  • Predicting dementia risk is crucial for timely intervention and resource allocation.
  • Leveraging routinely collected health insurance claims data offers a scalable approach to risk prediction.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting Alzheimer-type dementia risk in individuals over 75 years old.
  • To compare the effectiveness of sparse logistic regression with L0 and L1 regularization for feature selection in dementia risk prediction.
  • To identify influential risk factors for Alzheimer-type dementia using claim data.

Main Methods:

  • Utilized a dataset of 48,123 individuals from Japanese health and long-term care insurance claims.
  • Employed sparse logistic regression models with L0 (SLR-L0) and L1 (SLR-L1) regularization for classification.
  • Performed feature selection on age, sex, 502 diseases (ICD-10), and 107 prescription drug classes.
  • Integrated 100 predictors trained via bootstrap sampling for robust predictions.

Main Results:

  • Both SLR-L0 and SLR-L1 achieved similar predictive performance, with Area Under the ROC Curves (AUCs) of 0.663 and 0.660, respectively.
  • SLR-L0 selected an average of 13 features, significantly fewer than SLR-L1's average of 253 features.
  • SLR-L0 identified more influential features, whereas SLR-L1 included less relevant ones.

Conclusions:

  • Sparse logistic regression with L0 regularization (SLR-L0) is a promising method for identifying key risk factors for Alzheimer-type dementia.
  • SLR-L0's ability to perform effective feature selection makes it potentially more practical for clinical application and expert discussion compared to SLR-L1.
  • Routinely collected claim data, when analyzed with advanced machine learning techniques, can aid in predicting dementia risk in older adults.