# Artificial Intelligence-Based System for Retinal Disease Diagnosis
## 3 Experimental Results
### Efficiency Results of RB and SGB Classifiers  
We test two classifiers, considering them as competitors. It is required to evaluate their comparative efficiency for further selection of one of them. Performance evaluation is implemented based on two metrics--MAUC and MMCC. The results of Table 2 show that the SGB algorithm has a higher value of the average MAUC and MMCC, which means its higher efficiency, but for convincingness we will conduct additional statistical tests.  
For a more rigorous comparison of the differences between the classification algorithms, a set of statistical tests are performed on the experimental results to further validate their results and determine whether there is a significant difference between them. The analysis is performed using a parametric test--an independent two-sample Student's \(t\)-test for independent samples--and a non-parametric Wilcoxon test. Although parametric tests are more reliable than non-parametric ones, they are based on strict assumptions that are often violated in machine learning studies and make their results unreliable. Therefore, non-parametric tests are preferable for comparing the results of classifiers.  
Statistical analysis, Table 3, showed that the means (averages) for both MAUC and MMCC differ insignificantly and the null hypothesis of equality of means cannot be rejected, with the exception of comparing the mean values of the MMCC metrics of the compared classifiers using the Wilcoxon test at a significance level of 0.05. This suggests that in a number of situations, the SGB algorithm for diagnosing retinal diseases provides higher efficiency.  
Thus, the statistical tests for a significant difference between the mean values of the samples of the MAUC and MMCC metrics for the RB and SGB algorithms do not confirm their absolute effectiveness. Therefore, it is proposed to use both algorithms in combination when diagnosing retinal diseases, obtaining a diagnosis based on both classifiers. The system has two modes for setting up algorithms. The first mode is sequential--each classifier forms a diagnosis (both first and second opinion), which must then be approved by a doctor. The second mode is parallel--a weighted assessment of the diagnoses made by both algorithms is performed, in which the SGB algorithm has a probability of 0.65 and the RB algorithm 0.35.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator/ Classification Algorithm** & **RB** & **SGB** \\ \hline  & MAUC & \\ Average & 0.8788 & 0.9122 \\ Standard deviation & 0.0700 & 0.0370 \\  & MMCC & \\ Average & 0.6693 & 0.7640 \\ Standard deviation & 0.0661 & 0.0821 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of statistical characteristics evaluation for MAUC, MMCC metrics for two classifiers—RB and SGB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compared Classifiers** & \(\mathbf{t}\)**-Test Value** & **Wilcoxon Test Value** \\ \hline RB \& SGB (MAUC) & 1.03 & 1.36 \\ RB \& SGB (MMCC) & 2.2 & 1.99 * \\ \hline \hline \end{tabular}  
* statistically significant parameter for \(p<0.05\).  
\end{table}
Table 3: Results of parametric t-test and non-parametric Wilcoxon test.  
In continuation of research in this direction, a more detailed analysis of the data can be carried out to study the variation of the factors' (predictors) values in groups. It can be hypothetically assumed that the classifier based on the RB algorithm more accurately identifies the diagnosis in samples with higher variability of predictor values, while the classifier based on the SGB algorithm better diagnoses diseases with lower variations of predictors.