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Illustration of mental health analytics using AI and machine learning

IEEE Research Paper · Co-Author · Accepted

Machine Learning-Based Mental Health Assessment and Depression Screening Using the U.S. DASS-42 Dataset

International Conference on Emerging Digital Intelligence and Generative Engineering (ICEDIGE 2026) · October 30–31, 2026

PythonXGBoostRandom ForestANNICEDIGE 2026

📅 Accepted — presenting at the International Conference on Emerging Digital Intelligence and Generative Engineering (ICEDIGE 2026), October 30–31, 2026.

Identifying depression early can help ensure timely intervention and improved mental health outcomes. This paper presents a machine learning approach to binary depression classification using a U.S.-specific subset of the publicly available DASS-42 dataset.

Three models were trained and compared under the same pre-processing conditions: Random Forest, XGBoost, and an Artificial Neural Network (ANN).

Infographic summarizing the dataset, methodology, model comparison, and impact of the depression classification study
Study overview — dataset (3,730 balanced samples), methodology, model comparison, and real-world impact.

XGBOOST

98.72%

Accuracy

ANN

97.20%

Accuracy

RANDOM FOREST

92.94%

Accuracy

Confusion matrices for XGBoost, ANN, and Random Forest
Fig. 2 — confusion matrices for all three models on the held-out test set.
Accuracy and loss learning curves for the ANN across training epochs
Fig. 3 — ANN accuracy/loss curves across training epochs, showing stable convergence.

XGBoost was the top performer and was further tested on unseen samples, where it proved suitable for fast prediction — making it a potential tool for digital depression screening for students and young adults.

This paper has been accepted to ICEDIGE 2026 and is scheduled for presentation at the end of October 2026. This page will be updated with the IEEE Xplore link once it’s published.