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Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults

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  • Sri Susanty
  • Herdiantri Sufriyana
  • Emily Chia-Yu Su
  • Yeu-Hui Chuang

Abstract

The 15-item Geriatric Depression Scale (GDS-15) is widely used to screen for depressive symptoms among older populations. This study aimed to develop and validate a questionnaire-free, machine-learning model as an alternative triage test for the GDS-15 among community-dwelling older adults. The best models were the random forest (RF) and deep-insight visible neural network by internal validation, but both performances were undifferentiated by external validation. The AUROC of the RF model was 0.619 (95% CI 0.610 to 0.627) for the external validation set with a non-local ethnic group. Our triage test can allow healthcare professionals to preliminarily screen for depressive symptoms in older adults without using a questionnaire. If the model shows positive results, then the GDS-15 can be used for follow-up measures. This preliminary screening will save a lot of time and energy for healthcare providers and older adults, especially those persons who are illiterate.

Suggested Citation

  • Sri Susanty & Herdiantri Sufriyana & Emily Chia-Yu Su & Yeu-Hui Chuang, 2023. "Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-23, January.
    • Handle: RePEc:plo:pone00:0280330
    DOI: 10.1371/journal.pone.0280330
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