# Data Driven Approach for Eye Disease Classification with Machine Learning
## 4 Results  
A step-wise decision system generates the classification results presented in Tables 4 and 5. The performance of the classification algorithms was evaluated using several statistical measures, namely, kappa statistics, root mean squared error (RMSE), accuracy, precision, recall and the area under the curve (AUC) of receiver operating characteristics (ROC) graphs. More than one measure is usually considered when classifiers are compared because one measure, for example, accuracy, considers only correct predictions, whereas others, for example, RMSE, consider only false predictions. However, some measures consider both correct and false predictions, for example, precision and recall but by a different proportion. Therefore, most of the commonly used performance measures were considered. As expected, tree-based methods performed better than probability-based methods (Naive Byes). The kappa statistics demonstrated that both the decision tree and random forest algorithms performed better than the neural network. Similarly, according to the values of precision, recall and the F-measure, the random forest algorithm performed well. Conventionally, precision, recall and ROC are considered graphical measures, where curves are drawn to display the performance of an algorithm. For ROC curves, the true positive rate (TPR) is drawn against the false positive rate (FPR). However, to demonstrate the performance of the entire framework for predicting different classes (diseases), these performance measures for each class are shown in Figures 6-8. Precision is a measure of relevance, whereas recall is the fraction of retrieved relevant instances over the total number of relevant instances (obtained usually in the range 0--1); therefore, high values for both measures indicate better performance. Similarly, FPR is the fraction of wrong true predictions and TPR or sensitivity is the fraction of correct classifications (for a classifier). TPR and FPR also range between 0 and 1 and high TPR and low FPR reflect good performance. Figure 8 shows that the random forest algorithm yields a high TPR for more classes (diseases) compared to the decision tree and naive \begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Test** & **Decision** & **Naïve** & **Random** & **Neural** \\
**Statistics** & **Tree** & **Bayes** & **Forest** & **Network** \\ \hline Correctly Classified Instances & 85.81\% & 81.53\% & 86.63\% & 85.98\% \\ Incorrectly Classified Instances & 14.18\% & 18.47\% & 13.36\% & 14.02\% \\ Kappa Statistics & 0.84 & 0.79 & 0.85 & 0.81 \\ Mean Absolute Error & 0.029 & 0.038 & 0.0233 & 0.0779 \\ Root Mean Squared Error & 0.1204 & 0.1481 & 0.106 & 0.1857 \\ Relative Absolute Error & 19.69\% & 25.80\% & 15.81\% & 36.81\% \\ Root Relative Squared Error & 44.38\% & 54.55\% & 39.05\% & 57.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of four different classifiers using statistical measures. The averages are for 10-fold cross-validation results of a 52 classes (diseases) classification. \begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Performance Measures** & **Decision Tree** & **Naïve Byes** & **Random Forest** & **Neural Network** \\ \hline Accuracy(\%) & 85.81 & 81.53 & 86.63 & 85.98 \\ Precision & 0.874 & 0.816 & 0.889 & 0.857 \\ Recall & 0.858 & 0.815 & 0.866 & 0.86 \\ F-Measure & 0.85 & 0.809 & 0.861 & 0.856 \\ Computation time(seconds) & 0.11 & 0.05 & 6.3 & 5.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Classification Algorithms. The measures are averages of 10-fold cross-validation results produced from classifying 52 classes (diseases). Bayes algorithms. Although the FPR of the naive Bayes algorithm has the lowest value, its TPR is also low for a number of classes. Figure 6: Precision analysis of classification algorithms for 52 classes (diseases) shown in Table 6. Precision and recall values decrease from the outermost (value 1) to the innermost circle (value 0).