Early Detection of Diabetic Retinopathy Utilizing Advanced Fuzzy Logic Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The escalating prevalence of diabetes globally, exacerbated by lifestyle changes postpandemic-including increased screen time, sedentary behavior, and remote workhas consequently driven a surge in associated complications, notably, Diabetic Retinopathy (DR).This ocular complication presents a pressing concern due to its potential to precipitate irreversible vision loss.Consequently, the necessity for timely and accurate DR detection is paramount, especially in circumstances where conventional diagnostic approaches are either challenging or financially prohibitive.Capitalizing on the prowess of fuzzy logic in managing uncertainties, this study introduces an innovative application of Extended Fuzzy Logic for the early-stage detection of DR.Rather than focusing solely on overt symptoms, this approach discerns subtle similarities in retinal irregularities between diabetic patients and non-diabetic individuals.To quantify these similarities, the 'f-validity' value was computed based on DR risk factors and associated symptoms, which were subsequently transformed into membership function values.The aggregation of these values was facilitated by the Ordered Weighted Averaging (OWA) operator.The experimental outcomes of this approach align satisfactorily with expert anticipations, boasting an accuracy of 90%, a precision of 92.2%, and a sensitivity of 75%.These results, when juxtaposed against contemporary studies in the field, underscore the promise of this scheme in advancing early diagnostics of DR.The study thus proposes a potential solution that leverages the power of fuzzy logic to address the burgeoning challenge of DR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it