Harnessing Machine Learning for Ocular Disease Detection: Performance Evaluation of Gradient Boosting Models
Bibliographic record
Abstract
The risk of vision loss can be reduced by early diagnosis of ocular diseases and hence better patient outcomes. As the ophthalmic imaging data continues to increase, machine learning (ML) techniques are being used as effective method in elaborated disease diagnosis. The paper is dedicated to the implementation of the Gradient Boosting Models (GBMs) to the accurate discrimination and detection of different ocular diseases according to the fundus images features and patient metadata. The dataset which contained clinical characteristics like the age, sex, fundus images of both the eyes and related diagnostic keywords were examined. Data pre-processing and feature engineering have been performed to maximize the quality of data and derive the patterns of interest. Stratified k-fold cross-validation was used to provide robustness as well as avoiding over fitting using the GBM algorithm. The most important parameters by which the performance of the models was tested consisted of accuracy, precision, recall, Fl-score, and the area under the receiver operating characteristic curve (AUC-ROC). Findings indicate that GBMs have good diagnostic precision and generalizability rates concerning various ocular disorders. The present study presents the opportunity offered by ensemble learning, GBM in particular, to help ophthalmologists by giving them automated, trusted diagnostic support. The results can support applying ML models with interpretable behaviour to the clinical workflow to ensure the early diagnosis and treatment strategy in ophthalmology.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".