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ORIGINAL RESEARCH article

Front. Public Health
Sec. Public Mental Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1462387
This article is part of the Research Topic Exploring Mental Health in Vulnerable Populations in Developing Countries View all 37 articles

Machine Learning Algorithms to Predict Depression in Older Adults in China: A Crosssectional Study

Provisionally accepted
Yan Li Qing Song Yan Li Qing Song 1Lin Chen Lin Chen 1Haoqiang Liu Haoqiang Liu 1Yue Liu Yue Liu 2*
  • 1 Nanjing Tech University, nanjing, China
  • 2 Shanghai University of Sport, Shanghai, China

The final, formatted version of the article will be published soon.

    The twofold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's elderly population and to determine the noteworthy aspects resulting in depression.Methods: This research selected 7880 elderly people by utilizing data from the 2020 China Health and Retirement Longitudinal Study. Thereafter, the dataset was classified into training and testing sets at a 6:4 ratio. Six ML algorithms, namely, logistic regression, k-nearest neighbors, support vector machine, decision tree, LightGBM, and random forest, were used in constructing a predictive model for depression among the elderly. To compare the differences in the ROC curves of the different models, the Delong test was conducted. Meanwhile, to evaluate the models' performance, this research performed decision curve analysis (DCA). Thereafter, the Shapely Additive exPlanations values were utilized for model interpretation on the bases of the prediction results' substantial contributions.The range of the area under the curve (AUC) of each model's ROC curves was 0.648-0.738, with significant differences (P < 0.01). The DCA results indicate that within various probability thresholds, LightGBM's net benefit was the highest. Self-rated health, nighttime sleep, gender, age, and cognitive function are the five most important characteristics of all models in terms of predicting the occurrence of depression.The occurrence of depression among China's elderly population and the critical factors leading to depression can be predicted and identified, respectively, by ML algorithms.

    Keywords: Depression, machine learning, Health Promotion, CHARLS, China

    Received: 10 Jul 2024; Accepted: 10 Dec 2024.

    Copyright: © 2024 Song, Chen, Liu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yue Liu, Shanghai University of Sport, Shanghai, China

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