OVERALL MULTICLASS CLASSIFICATION REPORT: EEG-Based Alzheimer's and MCI Diagnosis Results

A research framework employing electroencephalography (EEG) to diagnose Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy subjects. The dataset, authored by Ashik Mostafa Alvi and last updated on April 20, 2026, contains performance metrics for the proposed Cognitive Decline Recognition Network (CDR-Net) architecture. The dataset is 9.5 KB in size and is stored in an XLS file format.

Use Cases

• Benchmarking multiclass classification models for EEG-based brain disorder diagnosis based on the reported accuracy, sensitivity, and specificity metrics.
• Analyzing the stability and consistency of a diagnostic framework based on the described 10-fold and leave-one-out cross-validation methods.
• Investigating feature extraction and classification techniques for EEG signals based on the described preprocessing and CDR-Net architecture.

Strengths

• Reported high multiclass performance metrics: 99.25% accuracy, 99.13% sensitivity, and 99.32% specificity.
• The methodology addresses data overfitting and underfitting concerns through cross-validation.
• 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 dataset is very small at 9.5 KB, indicating limited scope, likely containing only summary results.

Provenance

Source

figshare

Collection Method

Likely contains results from a research study employing a proposed CDR-Net architecture on EEG data.

Time Range

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Freshness

Last updated 2026-04-20 17:58:10; freshness should be verified.

Geography

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