Leveraging Explainable Artificial Intelligence to Identify Key Features required for Differentiating Clinical Neurocognitive Disorder Diagnoses using Toronto Cognitive Assessment
Bibliographic record
Abstract
Abstract Background Accurate clinical diagnosis for Alzheimer's disease dementia (AD), amnestic mild cognitive impairment (aMCI), and non‐amnestic MCI (naMCI) is essential for timely management. The diagnosis is made using a range of factors including cognitive testing. Explainable artificial intelligence (XAI)‐based SHAP (SHapley Additive exPlanations) is a machine learning interpretability tool that can provide insights into specific features that drive classification decisions. We used XAI with support vector machines (SVM) to identify key cognitive features of Toronto Cognitive Assessment (TorCA), a user‐friendly cognitive assessment administered by frontline clinicians, for differentiating neurocognitive disorder diagnoses. Method We used data from the Toronto Dementia Research Alliance (TDRA) database, comprising of participants with AD, aMCI, naMCI, or normal cognition (NC) seen in memory clinics across Toronto. An SVM model with radial basis function (RBF) kernel was configured with 10‐fold cross‐validation. XAI was integrated using SHAP values to identify the most important critical features contributing to the model predictions. Classification accuracies, defined as the proportion of correct classifications for each pairwise comparison, were calculated using TorCA total scores and specific features from subtests. Result We included 695 participants (149 AD, 189 aMCI, 304 naMCI, and 53 NC). Classification accuracy for distinguishing AD vs NC was excellent, whether using all TorCA subtests (0.97±0.03), or the top 5 features (0.96±0.04) (Delayed Recall, Immediate Recall Trials 1 and 2, Sentence Comprehension, Benson Figure Recall), but lower (0.93±0.06) with only TorCA total score. Classification accuracy for MCI or naMCI vs NC was also very good (0.86±0.03 to 0.89±0.04) using the top 5 features. Conclusion TorCA combined with XAI can accurately differentiate common clinical neurocognitive disorder phenotypes in ambulatory settings. These findings point to specific cognitive subtests important for diagnosis and may help improve the efficiency of cognitive testing. Future studies should investigate the differentiation of other neurocognitive disorders using these tools and further validate these findings using formal neuropsychological testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".