Alzheimer’s disease diagnosis using genetic programming based on higher order spectra features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Alzheimer’s diagnosis field, Computer-Aided Diagnosis (CADx) technology can improve the work performance of medical researchers and practitioners since it gives early chances to patient’s eligibility for clinical trials. The aim of this study is to develop a novel CADx system for the diagnosis of Alzheimer’s disease (AD) by utilizing genetic programming (GP) as data-driven evolutionary computation based modeling. The proposed method invokes a majority voting based scheme to select a set of most discriminant features which leads to the highest diagnosis accuracy of the final classification. The effectiveness of GP in categorizing patients with Alzheimer’s versus healthy group was revealed by developing models according to their performance in terms of higher-order spectra (HOS) features. The results show that the GP method achieved better performance compared to other the-state-of-the-art approaches. It is also found that the highest accuracy index was yielded by using the proposed data-driven modeling technique. The results of this study emphasize the practicality of GP-based method for developing CADx systems, on the basis of spontaneous speech analysis; can efficiently assist in the diagnosis of Alzheimer’s disease.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it