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. 2022 Jul 29:13:947856.
doi: 10.3389/fpsyg.2022.947856. eCollection 2022.

Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Xuan Luo  1 , Youlian Huang  1
Affiliations

Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Xuan Luo et al. Front Psychol. .

Retraction in

Abstract

COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children's and young people's mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children's and youths' mental states, a combination of heterogeneous data from practically all feature groups is required.

Keywords: COVID-19; artificial intelligence; clustering; feature selection; machine learning; mental health; mood state.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Architecture of proposed method.
Figure 2
Figure 2
Clustering methods used.
Figure 3
Figure 3
Feature selection method used.
Figure 4
Figure 4
For the first 40 features in which the greatest result was attained, a spider plot showing the amount of characteristics that correspond to every characteristic group was created.
Figure 5
Figure 5
Maximum accuracy for classification methods.
Figure 6
Figure 6
Following calibration using isotonic and SIRD model, change in expected probability on test samples.
Figure 7
Figure 7
XG Boost classifier calibration graph for class 0.
Figure 8
Figure 8
XG Boost classifier calibration graph for class 1.
Figure 9
Figure 9
XG Boost classifier calibration graph for class 3.
See this image and copyright information in PMC

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