# Data Driven Approach for Eye Disease Classification with Machine Learning
## 4 Results
### Discussion  
The proposed framework was aimed at collecting structured diagnostic data for the prediction of eye diseases by considering all possible symptoms of the anterior and posterior eye segments. Therefore, it was not evaluated for a specific disease; rather, all eye diseases mentioned in ICD-10 were predicted. Most of the current frameworks are designed for a specific eye disease, for example, refractive error, retinal detachment or diabetic retinopathy. By contrast, the purpose of this model is to develop a framework that can handle any type of data and predict all types of eye diseases based on standard symptom taxonomies. However, comparing it with the data mining framework for the prediction of fatty liver disease in Reference [57], where a number of classifiers were used, tree-based methods yielded the highest accuracy, as shown in Table 8. Furthermore, as in Reference [36], the proposed method facilitated the identification of the correlations between attributes related to specific diseases by combining the observations related to different eye segments into one symptom. Recently published work for heart failure identification from unstructured data [58] exhibits good accuracy, although NLP techniques were used to first convert unstructured data into structured data that was subsequently fed into classification algorithms. The accuracy they achieved using the decision tree and naive Bayes algorithms is significantly higher than that by the random forest and neural network algorithms but the results are based on only 14 patients' data. Therefore, reliability is questionable. In the present study, a substantially larger number of classes, hence more data, were considered, as compared to other studies. The comparison of the proposed method with existing solutions (shown in Table 8) demonstrated that this framework is capable of predicting eye diseases as accurately as analogous specialized frameworks presented for other medical diagnoses.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Diagnosis Level** & **Decision Tree** & **Naïve Bayes** & **Random Forest** & **Neural Network** \\ \hline Level 1 & 90.79 & 87.21 & 91.57 & 89.12 \\ Level 2 & 87.14 & 84.76 & 89.82 & 87.46 \\ Complete Diagnosis & 85.81 & 81.53 & 86.63 & 85.98 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy (%) for classification output at different levels of diagnosis.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Purpose** & **Performance** \\ \hline
[4] & Expert system for eye diseases using if-else rules & positive response from patients \\
[14] & Pattern recognition in data & sensitivity 89–94\% \& specificity \\
[57] & classification of liver disease using & accuracy 93.3 \% \\
[58] & unstructured data & heart failure identification using & accuracy 92\% \\
[58] & unstructured data & eye disease classification using structured data & accuracy 93.5 \% \\ Proposed method & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison.  
The results demonstrated that classification by tree algorithms had better prediction accuracy. This is because data was arranged in hierarchical structure and therefore the decision tree and random forest algorithms made better predictions using step-by-step analysis of pruned trees. Moreover, tree algorithms had lower error rate than other algorithms. The neural network algorithm also performed well owing to the multiple hidden layers involved. The results by the naive Bayes classifier were not as good as those of the others.