Feasibility Study: Machine Learning in Neurodegenerative Disorders, Alzheimer’s Disease
Bibliographic record
Abstract
Introduction: Clinical decision support systems (CDSSs), powered by machine learning and artificial intelligence, have demonstrated potential in clinical diagnosis and intervention for neurological and psychiatric disorders. Considering the importance of early detection and intervention of Alzheimer’s disease (AD), this study aims to explore the potential of a data-driven non-knowledge-based machine learning CDSS for predicting AD diagnoses in individuals. In non-knowledge-based CDSSs, no prior knowledge about AD or any other disorder impacts the decision-making of classification models Method: In this study, publicly available data of 14037 data points collected by the Alzheimer’s Disease Neuroimaging Initiative were used for model training and testing. Binary classification and multiclassification machine learning were applied, and results from six mainstream classification models were analyzed. Results: The binary classification models (AD diagnosis present or absent) gave accuracies around 0.92-0.93, and the multiclassification models gave accuracies around 0.85-0.87. Logistic regression model (binary classification) had the highest overall hit rate (0.93). This model maintained this hit rate when only features with over 90% non-empty data are available. Discussion: Binary classification models are more reliable for diagnosing AD than multiclassification models. The high hit rates of the logistic regression model (binary classification) on generally available data implicate its feasibility. Conclusion: There is strong potential for a complete machine learning-based CDSS to aid in AD diagnoses in the future
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".