A classification model for predicting diabetic retinopathy based on patient characteristics and biochemical measures

Kotsiliti, Evangelia, Hunter, Andrew and Al-Diri, Bashir (2017) A classification model for predicting diabetic retinopathy based on patient characteristics and biochemical measures. Journal for Modeling in Ophthalmology, 1 (4). pp. 69-85.

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A classification model for predicting diabetic retinopathy based on patient characteristics and biochemical measures
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Purpose: In the United Kingdom (UK), the National Health Service (NHS) Diabetic Eye Screening Program o ers an annual eye examination to all people with diabetes aged 12 and over aimed at the early detection of people at high risk of visual loss due to diabetic retinopathy. The purpose of this study was to design a model for predicting patients at risk of developing retinopathy using patient characteristics and clinical measurements.
Methods: We appraised data collected from the population-based Diabetic Eye Screening Program in East Anglia between 2011 and 2016. The data comprised reti- nal photographic screening results, patient characteristics of gender and age of the subject as well as duration and type of diabetes, and routine biochemical measures of Hemoglobin A1c (HbA1c), blood pressure, Albumin to Creatinine ratio (ACR), es- timated Glomerular Filtration rate (eGFR), serum creatinine, cholesterol, and Body Mass Index (BMI). Individuals were classified according to the presence or absence of retinopathy as indicated by their retinal photographic screening results. A logistic regression with Least Absolute Shrinkage and Selection Operator (lasso) regulariza- tion, random forest, gradient boosting machine, and regularized gradient boosting model were deployed and cross-validated for their predictive ability.
Results: A total of 6375 subjects with recorded information for all available biochemical measures were identified from the cohorts. Of these, 5969 individuals had no signs of diabetic retinopathy. Of the remaining 406 individuals with signs of diabetic retinopathy, 352 had background diabetic retinopathy and 54 had referable diabetic retinopathy. The highest value of the ten-fold cross-validated area under the curve (AUC) of the receiver operating curve (ROC), 0.73 ± 0.03, was achieved by the gradient boosting machine and the minimum required set of variables to yield this perfor- mance included: duration of diabetes, HbA1c, ACR, and age. A subsequent analysis on the predictive power of the biochemical measures showed that when HbA1c and ACR measurements were available for longer time periods, the performance of the models was enhanced. When HbA1c and ACR measurements for a five-year period prior to the event of study were available, gradient boosting machine cross-validated AUC was 0.77 ± 0.04 in comparison to the cross-validated AUC of 0.68 ± 0.04 when only information for the one-year period for these variables was available. Similarly, an increment from 0.70 ± 0.02 to 0.75 ± 0.04 was observed with random forest. The dataset with the one-year measurements comprised 4857 subjects, of whom 4572 had no retinopathy and the remaining 285 had signs of retinopathy. The dataset with the five-year measurements comprised 757 subjects, of whom 696 had no retinopathy and the remaining 51 had signs of retinopathy.
Conclusion: Patient information and routine biochemical measures can be used to identify patients at risk of developing retinopathy with a significant reduction of the number of screening visits.
Keywords: area under the ROC curve (AUC), classification, gradient boosting, lasso, prevalence and risk of diabetic retinopathy, random forest, retinal screening

Keywords:diabetic retinopathy, diagnosis, Classification
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
B Subjects allied to Medicine > B500 Ophthalmics
Divisions:College of Science > School of Computer Science
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ID Code:37994
Deposited On:23 Oct 2019 08:34

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