Expert system for predicting the stage of diabetic retinopathy based on the analysis of platelet dysfunction

Abstract

Aim: to develop an expert system for predicting the stage of diabetic retinopathy (DR) on the basis of determination of the platelet dysfunction using the methods of multi-factor neural network and logistic regression modelling at the time of the initial examination of the patient. Material and methods. The model was built (Statistica Neural Network v.4.0B) based on the results of clinical-laboratory study of 99 patients (99 eyes) with type 2 diabetes and DR. The evaluation of aggregation was carried out by spectrophotometric method on a ChronoLog (USA) aggregometer. The study used agonists (Sigma, USA): adenosine diphosphate (ADP, 2.5 μM); adrenaline (2.5 μM); angiotensin-2 (An-2, 1 μM); the platelet activation factor (PAF, 75 μM) and collagen (2.0 mg/ml). Agonists were used in an effective concentration (EC50), which caused the platelet aggregation at 50±5% in healthy individuals (10 donors). Results and discussion. To determine the leading determinants of the development of the DR stages, a mathematical analysis was carried out using the methods of constructing multi-factor neural network and logistic regression models. The prediction of the DR stage in a two-factor linear neural network model was based on platelet aggregation induced by ADP and collagen; the forecast accuracy was 81.8% (95% CI 73.5-88.8%). Four-factor nonlinear neural network MLP-model (An-2, ADP, adrenaline and collagen induced aggregation) was constructed, which allowed to increase the prediction accuracy of the DR stage to 93.9% (95% CI 88.3-97.8%). Conclusions. For the first time, on the basis of the definition of platelet aggregation, an expert system based on the construction of neural network models is proposed. With a high accuracy of prediction of the model, it is possible to predict the development of the DR stage even at the initial treatment of the patient.