High Accuracy Diagnosis for MRI Imaging Of Alzheimer’s Disease using Xgboost
Bibliographic record
Abstract
Introduction: Alzheimer’s disease (AD) is the most epidemic type of dementia. The cause and treatment of the disease remain unidentified. However, when the impairment is still at a preliminary stage or mild cognitive impairment (MCI), the symptoms might be more controlled, and the treatment can be more efficient. As a result, computational diagnosis of the disease based on brain medical images is crucial for early diagnosis. Methods: In this study, an efficient computational method was introduced to classify MRI brain scans for patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal aging control (NC), comprising three main steps: I) feature extraction, II) feature selection III) classification. Although most of the current approaches utilize binary classification, the proposed model can differentiate between multiple stages of Alzheimer’s disease and achieve superior results in early-stage AD diagnosis. 158 magnetic resonance images (MRI) were taken from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), which were preprocessed and normalized to be suitable for extracting the volume, cortical thickness, sulci depth, and gyrification index measures for various brain regions of interest (ROIs), as they play a considerable role in the detection of AD. One of the embedded feature selection method was used to select the most informative features for AD diagnosis. Three models were used to classify AD based on the selected features: an extreme gradient boosting (XGBoost), support vector machine (SVM), and K-nearest neighborhood (KNN). Results and Discussion: XGBoost showed the highest accuracy of 92.31%, precision of 0.92, recall of 0.92, F1-score of 0.92, and AUC of 0.9543. Recent research has reported using multivariable data analysis to classify dementia stages such as MCI and AD and employing machine learning to predict dementia stages. Conclusion: In the proposed method, we achieved good performance for early-stage AD (MCI) detection, which is the most targeted stage to be identified. Moreover, we investigated the most reliable features for the diagnosis of AD.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".