Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386997081> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W4386997081 abstract "Objective: This study aimed to develop a predictive risk score model based on deep learning (DL) independent of fundus photography, totally reliant on systemic data through targeted screening from a population-based study to diagnose diabetic retinopathy (DR) in the Indian population. Methods: It involved machine learning application on datasets of a cross-sectional population-based study. A total of 1425 subjects (1175 subjects with known diabetes and 250 with newly diagnosed diabetes) were included in the study. We applied five machine learning algorithms, random forest (RF), logistic regression (LR), support vector machines (SVM), artificial neural networks (ANN), and decision trees (DT), to predict diabetic retinopathy in our datasets. We incorporated a percentage split in the first experiment and randomly divided our data set into 80% as a training set and 20% as a test set. We performed a three-way data split in the second experiment to prevent overestimating predictive performance. We randomly divided our data set into 60% as a training set, 20% as a validation set, and 20% as the test set. Furthermore, we integrated five-fold cross-validation to split the percentage to evaluate our method. We judged the predictive performance based on the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy (Acc), sensitivity, and specificity. Results: The RF classifier achieved the best prediction performance with AUC, Acc, and sensitivity values of 0.91, 0.89, and 0.90, respectively, in the percentage split. Similarly, a three-way data split attained an outcome of 0.86 and 0.85 in AUC and Acc. Likewise, the five-fold cross-validation performed the best with results of 0.90, 0.97, 0.91, and 0.75 in AUC, Acc, sensitivity, and specificity, respectively. Conclusion: Since the RF classifier achieved the best performance, we propose it to identify diabetic retinopathy for targeted screening in the general population." @default.
- W4386997081 created "2023-09-25" @default.
- W4386997081 creator A5032145602 @default.
- W4386997081 creator A5036238884 @default.
- W4386997081 creator A5051509488 @default.
- W4386997081 creator A5060172197 @default.
- W4386997081 date "2023-09-24" @default.
- W4386997081 modified "2023-09-30" @default.
- W4386997081 title "Developing a Risk Stratification Model Based on Machine Learning for Targeted Screening of Diabetic Retinopathy in the Indian Population" @default.
- W4386997081 cites W1577487871 @default.
- W4386997081 cites W1970005405 @default.
- W4386997081 cites W197570494 @default.
- W4386997081 cites W1978022072 @default.
- W4386997081 cites W1991669891 @default.
- W4386997081 cites W2030811942 @default.
- W4386997081 cites W2046514232 @default.
- W4386997081 cites W2164949078 @default.
- W4386997081 cites W2338167986 @default.
- W4386997081 cites W2498119267 @default.
- W4386997081 cites W2504867442 @default.
- W4386997081 cites W2598992821 @default.
- W4386997081 cites W2885568874 @default.
- W4386997081 cites W2920497957 @default.
- W4386997081 cites W2946387978 @default.
- W4386997081 cites W3040016603 @default.
- W4386997081 cites W3088051685 @default.
- W4386997081 cites W3204734758 @default.
- W4386997081 cites W3211128217 @default.
- W4386997081 cites W4206514848 @default.
- W4386997081 cites W4246424096 @default.
- W4386997081 doi "https://doi.org/10.7759/cureus.45853" @default.
- W4386997081 hasPublicationYear "2023" @default.
- W4386997081 type Work @default.
- W4386997081 citedByCount "0" @default.
- W4386997081 crossrefType "journal-article" @default.
- W4386997081 hasAuthorship W4386997081A5032145602 @default.
- W4386997081 hasAuthorship W4386997081A5036238884 @default.
- W4386997081 hasAuthorship W4386997081A5051509488 @default.
- W4386997081 hasAuthorship W4386997081A5060172197 @default.
- W4386997081 hasBestOaLocation W43869970811 @default.
- W4386997081 hasConcept C118487528 @default.
- W4386997081 hasConcept C119857082 @default.
- W4386997081 hasConcept C12267149 @default.
- W4386997081 hasConcept C126322002 @default.
- W4386997081 hasConcept C134018914 @default.
- W4386997081 hasConcept C151956035 @default.
- W4386997081 hasConcept C154945302 @default.
- W4386997081 hasConcept C169258074 @default.
- W4386997081 hasConcept C169903167 @default.
- W4386997081 hasConcept C2776474195 @default.
- W4386997081 hasConcept C2778257484 @default.
- W4386997081 hasConcept C2779829184 @default.
- W4386997081 hasConcept C2780248432 @default.
- W4386997081 hasConcept C2908647359 @default.
- W4386997081 hasConcept C41008148 @default.
- W4386997081 hasConcept C50644808 @default.
- W4386997081 hasConcept C555293320 @default.
- W4386997081 hasConcept C58471807 @default.
- W4386997081 hasConcept C58489278 @default.
- W4386997081 hasConcept C71924100 @default.
- W4386997081 hasConcept C95623464 @default.
- W4386997081 hasConcept C99454951 @default.
- W4386997081 hasConceptScore W4386997081C118487528 @default.
- W4386997081 hasConceptScore W4386997081C119857082 @default.
- W4386997081 hasConceptScore W4386997081C12267149 @default.
- W4386997081 hasConceptScore W4386997081C126322002 @default.
- W4386997081 hasConceptScore W4386997081C134018914 @default.
- W4386997081 hasConceptScore W4386997081C151956035 @default.
- W4386997081 hasConceptScore W4386997081C154945302 @default.
- W4386997081 hasConceptScore W4386997081C169258074 @default.
- W4386997081 hasConceptScore W4386997081C169903167 @default.
- W4386997081 hasConceptScore W4386997081C2776474195 @default.
- W4386997081 hasConceptScore W4386997081C2778257484 @default.
- W4386997081 hasConceptScore W4386997081C2779829184 @default.
- W4386997081 hasConceptScore W4386997081C2780248432 @default.
- W4386997081 hasConceptScore W4386997081C2908647359 @default.
- W4386997081 hasConceptScore W4386997081C41008148 @default.
- W4386997081 hasConceptScore W4386997081C50644808 @default.
- W4386997081 hasConceptScore W4386997081C555293320 @default.
- W4386997081 hasConceptScore W4386997081C58471807 @default.
- W4386997081 hasConceptScore W4386997081C58489278 @default.
- W4386997081 hasConceptScore W4386997081C71924100 @default.
- W4386997081 hasConceptScore W4386997081C95623464 @default.
- W4386997081 hasConceptScore W4386997081C99454951 @default.
- W4386997081 hasLocation W43869970811 @default.
- W4386997081 hasOpenAccess W4386997081 @default.
- W4386997081 hasPrimaryLocation W43869970811 @default.
- W4386997081 hasRelatedWork W3159096857 @default.
- W4386997081 hasRelatedWork W3195168932 @default.
- W4386997081 hasRelatedWork W4200096243 @default.
- W4386997081 hasRelatedWork W4226239449 @default.
- W4386997081 hasRelatedWork W4280641190 @default.
- W4386997081 hasRelatedWork W4319430317 @default.
- W4386997081 hasRelatedWork W4321636153 @default.
- W4386997081 hasRelatedWork W4367335893 @default.
- W4386997081 hasRelatedWork W4383535405 @default.
- W4386997081 hasRelatedWork W4384520063 @default.
- W4386997081 isParatext "false" @default.
- W4386997081 isRetracted "false" @default.
- W4386997081 workType "article" @default.