A Multi-Layer Perceptron Network-Based Model for Classifying Stages of Alzheimer's Disease Using Clinical Data
Bibliographic record
Abstract
Alzheimer's Disease is a neurodegenerative disorder that progressively impairs individuals' ability to perform daily activities.This irreversible condition cannot be halted once initiated, but early detection may allow for treatments to slow its progression.In this study, clinical data from the Alzheimer's Disease Neuroimaging Initiative dataset were utilized to identify different stages of Alzheimer's and predict the time required for conversion from mild cognitive impairment (MCI) to Alzheimer's Disease.Clinical indicators of Alzheimer's include age, education level, disease progression rate, and cognitive information.Machine learning techniques such as multi-layer perceptron networks, random forests, support vector machines, and decision tree classifiers were employed for binary and multi-class classification of Alzheimer's Disease (AD), Late Mild Cognitive Impairment (LMCI), Early Mild Cognitive Impairment (EMCI), and Cognitive Control (CN).Among these techniques, the multi-layer perceptron network demonstrated superior performance, achieving accuracies of 99.97% for AD vs LMCI, 99.57% for AD vs EMCI, 99.96% for AD vs CN, 95.05% for EMCI vs CN and LMCI vs CN, 99.97% for AD vs LMCI vs CN, 91.2% for EMCI vs LMCI vs AD, 86.25% for CN vs EMCI vs LMCI, 91.94% for CN vs LMCI vs AD, 85.14% for CN vs EMCI vs AD, and 77.5% for AD vs LMCI vs EMCI vs CN.The proposed model has the potential to facilitate early detection and prediction of Alzheimer's stages without the need for imaging scans, thus offering a valuable tool for clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".