Genetically Optimized Neural Network for Early Detection of Glaucoma and Cardiovascular Disease Risk Prediction
Bibliographic record
Abstract
Glaucoma, a major ocular disease, often culminates in irreversible blindness if undiagnosed.Characterized by optic nerve fiber degeneration, it instigates structural changes to the optic nerve, thus precipitating vision loss.Despite being symptomless, early detection is imperative to prevent further visual impairment.The disease's inception is attributed to increased intraocular pressure, a condition influenced by blood pressure.Notably, the eye and heart share parallel characteristics, making glaucoma an early indicator of potential cardiac conditions.Increased blood pressure frequently accompanies Diabetes mellitus -a common complication exacerbating cardiac health and fostering the development of cardiovascular diseases.Leveraging computational technologies allows for the early-stage identification of glaucoma.The utilization of deep learning approaches and pruning techniques has yielded significant outcomes in detecting glaucoma-related abnormalities accurately.Pruning, a strategy implemented to eliminate redundant parameters while preserving optimal performance, is particularly beneficial.This study introduces a Genetically Optimized Neural Network (GONN) incorporating wavelet transformation for the detection of glaucoma, thereby assisting in diabetes and heart disease risk identification.Experimental results demonstrate that the GONN method outperforms conventional methods such as Artificial Neural Networks (ANN), Naï ve Bayes, multilayer perceptron, ensemble methods, K-Nearest Neighbor (KNN), and decision trees.Notably, the GONN technique achieves an impressive accuracy of 95%, an F1 score of 92%, and an Area Under the Curve (AUC) of 98.92%.This study's findings underscore the potential of the GONN technique in accurately identifying glaucoma, thereby aiding in early diabetes and heart disease risk prediction.The results demonstrate that the GONN approach is a viable tool for clinical practice, with potential implications for improved patient outcomes and healthcare efficiency.
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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 | 0.000 | 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 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".