Leveraging data augmentation for machine learning models in predicting depression and anxiety using the Revised Child Anxiety and Depression Scale clinical reports.

ABSTRACT:

Objective

An estimated 15 million people are affected by depression and anxiety in Pakistan. However, there are relatively few government mental health facilities and certified psychiatrists. This highlights the need for efficient assessments to implement intervention strategies and address these challenges. This study aims to utilize machine learning with RCADS to maximize the use of current healthcare resources and facilitate depression and anxiety screening.

Methods

The dataset include 138 cases, with 89 retained after cleaning along 47 RCADS-items as features. Based on RCADS-47 T-scores, cases were classified as normal, borderline and clinical, with 7% in the borderline, 55% in normal and 38% in clinical range. Three feature selection methods - the Chi-square test of ind

SUBMITTER: Noor S 

PROVIDER: S-EPMC12696576 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature