Explainable Neural Network Prediction of Post-COVID-19 Depression via Monte Carlo Simulation

The COVID-19 pandemic has driven a substantial rise in depression, notably in Argentina’s post-quarantine period, motivating the need for predictive tools to support timely mental health interventions. This study uses a Feedforward Neural Network (FNN) and Monte Carlo simulations to predict depression scores from key socio-economic and psychologicalvariables—anxiety state, economic income, and education—benchmarked against SVR, GRU, Linear Regression, Decision Tree, and Random Forest. The FNN achieved the best overall performance (MAE = 4.72, RMSE = 6.32, R2 = 0.64; cross-validated R2 = 0.593 ± 0.048), while Linear Regression attained the highest R2 (0.693), suggesting partly linear relationships among predictors. Monte Carlo simulations showed that higher anxiety increased predicted depression, while higher income and education reduced it, underscoring the value of targeted anxiety-reduction and economic-support interventions in post-pandemic mental health policy.

@article{AKIL2026,
  title     = {Explainable Neural Network Prediction of Post-COVID-19 Depression via Monte Carlo Simulation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Siham AKIL and Sara SEKKATE and Abdellah ADIB},
  doi       = {10.14569/IJACSA.2026.0170698},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170698}
}