NeuroHAGWO-Net Model Performance Metrics on Behavioral Data for Mental Health Detection

Performance Comparison (Behavioral Data) contains evaluation metrics for the NeuroHAGWO-Net AI model, which uses multimodal data for automated mental health status detection. The dataset, authored by Sunil Kumar Sharma and last updated in April 2026, is a 5.5 KB Excel file with performance figures from model validation on behavioral data.

Use Cases

• Benchmarking AI model performance for mental health detection based on reported accuracy, precision, and F1-score metrics.
• Analyzing the efficacy of hybrid optimization techniques like HAGWO for feature selection in behavioral data.
• Comparing multimodal (EEG and text) versus unimodal (behavioral text) model performance for clinical support applications.

Strengths

• Model performance on behavioral data is quantified with nine specific metrics, including an accuracy of 0.9945.
• Dataset is openly licensed under CC-BY-4.0, facilitating reuse and sharing.
• File is small (5.5 KB), ensuring quick download and easy inspection.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset contains only performance metrics, not the underlying behavioral or EEG data used for model training/validation.

Provenance

Source

Sunil Kumar Sharma via figshare

Collection Method

Results from validating the proposed NeuroHAGWO-Net AI framework.

Time Range

Unknown

Freshness

Last updated 2026-04-13 17:44:20; freshness should be verified.

Geography

Unknown