# Artificial Intelligence-Based System for Retinal Disease Diagnosis
## 3 Experimental Results  
### RB-Classifier Testing  
The system is tested using actual data on retinal diseases from the Russian Institute of Eye Diseases. The original data set includes information about all described factors for 101 patients and their diagnoses. The fuzzy rule base is designed on an analysis of specialized literature and studies proving the dependence of detection of a specific pathology on deviation of certain ERG parameters from the normal value. In addition to the ERG parameters, the patient's symptoms and subjective sensations were selected, as well as their social and demographic factors, which can predetermine (stimulate) the appearance of a particular pathology. Each rule contains no more than five input variables, which include variables from at least two groups, since a diagnosis based only on socio-demographic, subjective and/or objective electrophysiological data will not reflect a reliable diagnosis. The combination of variables is selected so that the rule contains the most significant ERG indicators for diagnosing a specific pathology, as well as one or two factors from the first two groups, which are predetermining for certain pathology or are the most common symptoms. Different combinations of factors can generate different diagnoses. To study such multivariant combinations of factors that influence the confirmation or refutation of a particular diagnosis, we conduct a situational analysis. Simulation experiments are implemented in various situations close to real practice and requiring a decision on the diagnosis of retinal pathology. Multivariant situations with a different set of combinations of input factors of seven types are considered. The first group. In this situation, we vary the values of the input variables I17, I18, I23, I26, I30 and I38:  
* I17 (maximum ERG, peak latency of the b-wave)--\"norm\";
* I18 (maximum ERG, a-wave amplitude), I23 (rod ERG, b-wave amplitude), I26 (mf-ERG, retinal density of the P1 component)--\"under normal\";
* I30 (PERG, P50 time), I38 (latency of the a-wave of local ERG)--\"above normal\". Such a set of values of variables entered into the fuzzy model provides generation of diagnosis O1 \"proliferative diabetic retinopathy\". It should be noted that the study [28] confirms the significance of deviations from the norm of the results of multifocal ERG, ERG pattern, as well as maximum ERG for the diagnosis of diabetic retinopathy, which confirms the modeling results. The second group. In this situation we vary of the values of the input variables I1, I3, I4, I16, I19, I25, I26 and I27:  
* I1 (age)--\"41-60\"; I3 (diabetes mellitus)--\"2\";
* I4 (duration of diabetes mellitus, years)--\"5-10\";
* I16 (maximum ERG, b-wave amplitude)--\"reduced\"; I19 (maximum ERG, peak latency of the a-wave), I25 (mf-ERG, latency of the P1 component), I27 (mf-ERG, latency of N1)--\"above normal\";
* I26 (mf-ERG, retinal density of the P1 component)--\"under normal\". With this set of input variables, the model forms the diagnosis O1 \"non-proliferative diabetic retinopathy\". In this group of situations and experiments, the main determinants that form this diagnosis are diabetes mellitus and its duration, since it is mandatory and predetermines the conditions for the occurrence of diabetic retinopathy. The third group of situations determined the set of values of the input variables I1, I3, I16, I23, I26 and I28:  
* I1 (age)--\"61-80\"; I3 (diabetes mellitus)--\"not diagnosed\";
* I16 (maximum ERG, b-wave amplitude)--\"normal\";
* I23 (rod ERG, b-wave amplitude),
* I26 (mf-ERG, retinal density of the P1 component),
* I28 (PERG, amplitude)--\"under normal\". This group of experiments investigated the output variable O1, which determines the absence of pathology. Despite the absence of a key factor for the development of diabetic retinopathy--diabetes mellitus--, not all objective parameters are within normal limits.