# Data Driven Approach for Eye Disease Classification with Machine Learning
## 3 Methodology Adopted
### Methods  
A number of classification algorithms are in use in the medical domain with the promise of increasing reliability and confidence [50; 51]. One can easily select a classifier according to the data, whether parametric or non-parametric [52; 53]. Tree based methods such as Decision Tree and Random Forest are non-parametric classifiers and, therefore, can be applied to the data whose distribution  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Number of Attributes**} & \multicolumn{1}{p{42.7 pt}}{**Number of Instances**} \\
**Ophthalmology data:** & **Simptoms and Diagnosis** & **10** & **3025** & \\ \hline \hline \multirow{2}{*}{**S.No.**} & **Attribute Name** & **Attribute Type** & **Example** & **Attribute Description** \\ \hline
1 & Age & Numeric & 32 & Patient age \\ \hline
2 & Gender & Nominal & M & M/F \\ \hline
3 & Complain & Nominal & Blurred Vision, Pain history of present illness & Provides general health information and history of present illness \\ \hline
4 & VA\_OD & Nominal & 20/50 & Visual acuity of right eye to check if there is any problem in vision \\ \hline
5 & VA\_OS & Nominal & 20/40 & Visual acuity of left eye to check if there is any problem in vision \\ \hline
6 & PH\_OD & Nominal & 20/25 & Pinhole value of right eye denoting if vision abnormality improves on wearing glasses or not \\ \hline
7 & PH\_OS & Nominal & 20/20 & Pinhole value of left eye denoting if vision abnormality improves on wearing glasses or not \\ \hline
8 & Slit Lamp & Nominal & Lids; Swelling; Site; Upper Lid examination of patient showing external exam & Symptoms obtained from slit lamp examination of eye \\ \hline
9 & Posterior Segment & Nominal & Retina; Retinal Detachment; Rheumatous magenta Macula On & Examination of posterior segment of eye also outputs symptoms regarding macula, retina, optic-nerve vitreous and choroid-uveal. \\ \hline
10 & Diagnosis Class & Nominal & Glaucoma ; Unspecified primary angle-closure glaucoma & Diagnostic class is diagnosed based on symptoms shown above for prediction ICD-10 coding. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset and attributes description in not know. As in the medical domain collecting normally distributed data is almost impossible, non-parametric methods are always helpful [54]. However, some other classifiers such as Neural Network, Naive Bayes and Support Vector Machines have also been very popular in classifying diagnostic data but use assumptions to simplify the learning process and sometimes lead to a higher error rate. All the same, for some classes of disease, such as for cancers, Support Vector Machines can have a valuable role and to that end the reader is referred to Reference [55]. Herein, we have used a mix of both types of classification algorithms that include decision tree, random forest, naive Bayes and neural networks. The purpose is to identify the impact of the proposed data modeling method upon classification accuracy. Weka [56] was used for model implementation in the data classification algorithms. The Weka workbench is a combination of various machine-learning algorithms and data pre-processing filters, whereby existing complex learning algorithms can be applied to new datasets. As in most related studies, the dataset used in this study (described in Table 3) was divided into two sets: (1) Training; and (2) Testing with 70:30 split. For validation a 10-fold cross-validation method was used in the classification algorithms. For general comparison of the algorithms, no manual tuning of the parameters was performed so that the model can adjust itself according to the data.