NeuroHAGWO-Net Model Performance Metrics for Mental Health Detection

5.5 KB of tabular data containing performance metrics for the NeuroHAGWO-Net AI model, which integrates EEG signals and behavioral text for mental health screening. The dataset, authored by Sunil Kumar Sharma and last updated in April 2026, reports metrics including an accuracy of 0.9945 and an F1-Score of 0.9909 from validation on two datasets.

Use Cases

• Benchmarking multimodal AI model performance based on reported accuracy, precision, and F1-score metrics.
• Analyzing feature selection algorithm efficacy based on the described Hybrid Ant-Grey Wolf Optimization (HAGWO) approach.
• Comparing deep learning architectures for mental health detection based on the integrated EfficientNetV2 and Temporal CNNs (T-CNNs) mentioned.
• Studying the fusion of EEG and text data for clinical screening based on the described multimodal framework.

Strengths

• Performance metrics are highly specific, including accuracy (0.9945), precision (0.9874), and Matthews Correlation Coefficient (0.9925).
• Model validation is described on two distinct data modalities: behavioral data and EEG signals.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size suggests a very limited scope, likely containing only summary results, not raw data.

Provenance

Source

figshare, authored by Sunil Kumar Sharma.

Collection Method

Results from validating a proposed AI model (NeuroHAGWO-Net) on behavioral and EEG datasets.

Time Range

The dataset itself was updated in 2026; the temporal coverage of the underlying study data is unknown.

Freshness

Last updated 2026-04-13 17:44:03; freshness should be verified.

Geography

Spatial coverage of the underlying behavioral and EEG data is unknown.