Abstract:
Mental health issues, particularly early-onset psychopathology, are a neglected domain
of public health. In low middle-income countries like Pakistan, the management of
these problems is hindered by stigma, limited mental healthcare resources, and
protracted nature of consultations. The access and convenience of mental healthcare
can be improved by leveraging Artificial Intelligence (AI) for the development of datadriven decision support systems. This study aims to develop Machine Learning (ML)
based predictive models for psychosocial dysfunction in Pakistani children and
adolescents. The Pediatric Symptom Checklist (PSC) is used to collect data of 2,372
individuals. To the best of our knowledge, this study is the first to use data of Pakistani
pediatric populations collected using the PSC. Feature selection methods reveal that
items related to attention issues, hopelessness, sadness, and irritability are the most
significant predictors of psychosocial dysfunction for both age groups. Exclusively for
children, externalizing problems like disobedience are also significant. Six ML
algorithms viz. Decision Tree, Random Forest, XGBoost, Support Vector Machine,
Multilayer Perceptron, and Logistic Regression are selected to develop the predictive
models. Among these, Logistic Regression provides the best results with accuracies of
0.98 and 0.99 for children and adolescents, respectively. For children, it is observed
that exclusion of item 23 of the PSC (Wants to be with you more than before) improves
model performance. Our encouraging findings suggest that the proposed models have
the potential to be deployed in healthcare facilities and educational institutes for early
and accurate detection of pediatric psychosocial dysfunction.
