Olfactory examination for AD diagnosis based on machine learning
Bibliographic record
Abstract
Objective: To explore the feasibility of olfactory examination as an early diagnostic indicator of Alzheimer's disease (AD) by using machine learning method. Methods: A total of 100 dementia patients who were treated in the outpatient department of neurology of Tianjin Huanhu Hospital from April to November 2021 were collected, including 50 cases of MCI and 50 cases of AD, aged 50-86 years. The control group consisted of 50 healthy adults from the physical examination center, aged 50-79 years. Subjects were tested for olfactory subjective function test (tested with Sniffin Sticks), Montreal Cognitive Assessment Scale (MoCA), Mini-Mental State Examination (MMSE) and Adaptive Behavior Rating Scale (ADL). SPSS 20.0 statistical software was used to compare the correlations between the scores of the three groups of olfactory function tests and scale tests, and MATLAB 2014 software was used to classify the three groups of subjects using machine learning algorithms. Results: The olfactory function score and the cognitive function scale score showed moderate to strong correlation (>0.44). The classification accuracy of support vector machine (SVM) for the patient group and the control group reached 99.3%, and the accuracy of K-nearest neighbor algorithm (KNN) for MCI and AD reached 91.5%. Conclusion: The detection of olfactory function has certain predictive value in the diagnosis and transformation of MCI and AD, and can be used as a routine biomarker test.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".