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. 2024 Apr 24;12(1):230.
doi: 10.1186/s40359-024-01696-8.

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model

Yiwei Zhou  1   2   3 , Zejie Zhang  4 , Qin Li  5 , Guangyun Mao  6 , Zumu Zhou  7
Affiliations

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model

Yiwei Zhou et al. BMC Psychol. .

Abstract

Objectives: COVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness.

Methods: A cross-sectional method was used to examine the depression status of individuals quarantined at home during the epidemic via the network. Characteristics included variables on sociodemographics, COVID-19 and its prevention and control measures, impact on life, work, health and economy after the city was sealed off, and PHQ-9 scale scores. The home-quarantined subjects were randomly divided into training set and validation set according to the ratio of 7:3, and the performance of different machine learning models were compared by 10-fold cross-validation, and the model algorithm with the best performance was selected from 15 models to construct and validate the depression prediction model for home-quarantined subjects. The validity of different models was compared based on accuracy, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC), and the best model suitable for the data framework of this study was identified.

Results: The prevalence of depression among home-quarantined individuals during the epidemic was 31.66% (202/638), and the constructed Adaboost depression prediction model had an ACC of 0.7917, an accuracy of 0.7180, and an AUC of 0.7803, which was better than the other 15 models on the combination of various performance measures. In the validation sets, the AUC was greater than 0.83.

Conclusions: The Adaboost machine learning algorithm developed in this study can be used to construct a depression prediction model for home-quarantined individuals that has better machine learning performance, as well as high effectiveness, robustness, and generalizability.

Keywords: Adaboost Algorithm; COVID-19; Home-quarantined individuals; Machine learning.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the Adaboost algorithm
Fig. 2
Fig. 2
Order of the importance of various features
Fig. 3
Fig. 3
Recursive feature elimination to determine features
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Fig. 4
AUC curve of Adaboost model in validation sets
Fig. 5
Fig. 5
Calibration curves for the Adaboost model
See this image and copyright information in PMC

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