A NOVEL FRAMEWORK FOR THE CLASSIFICATION AND DETECTION OF ALZHEIMER'S DISEASE USING HYBRID MACHINE LEARNING MODELS
Bibliographic record
Abstract
Abstract Alzheimer's disease (AD) represents a profound challenge in neurodegenerative disease research due to its complex pathology and lack of definitive early diagnostic tools. This paper presents a novel framework for the classification and detection of Alzheimer's disease leveraging hybrid machine learning models. Our approach integrates convolutional neural networks (CNNs) for feature extraction from neuroimaging data with ensemble learning methods to enhance classification accuracy. We utilized a comprehensive dataset comprising MRI and PET scans from multiple sources, ensuring robust model training and validation. The proposed hybrid model demonstrated superior performance compared to traditional machine learning techniques, achieving high accuracy, sensitivity, and specificity. Furthermore, our model's interpretability is enhanced through feature importance analysis, providing insights into the key biomarkers associated with Alzheimer's disease. This framework holds significant potential for improving early diagnosis and facilitating targeted therapeutic interventions, ultimately contributing to better patient outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".