An Evaluation of Machine Learning Classifiers for Prediction of Alzheimer's Disease, Mild Cognitive Impairment and Normal Cognition
Bibliographic record
Abstract
Dementia has a negative impact on global healthcare that has become a serious concern worldwide. The most common cause, Alzheimer's disease, underlies the majority of dementia. The identification and accurate prediction of Alzheimer's disease in its initial stage is most critical, of which has several important practical applications. However, a reliable diagnosis remains a challenging task and requires a combination of methods based on important clinical information. Developing computer-aided diagnosis systems to support early Alzheimer's disease detection is essential for effective treatment planning. In this study, a nationwide cohort dataset, the Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease is classified by using eight state-of-the-art supervised machine learning algorithms namely Support Vector Machine, Naive Bayes, XGBoost, Decision Tree, Logistic Regression, Random Forest, Bagging and AdaBoost. The best performing model appeared to be the XGBoost classifier yielding an accuracy of 82.09%. Thus, the present research shows that the application of the machine learning model to the KBASE dataset will offer an efficient clinical classification of cognitively normal control individuals, mild cognitive impairment and Alzheimer's disease patients and provides a framework for clinical decision systems.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".