<p class=\"MsoNormal\" style=\"text-align: justify; text-justify: inter-ideograph; text-indent: 36.0pt;\"><span style=\"font-size: 12.0pt; line-height: 115%; font-family: \'Times New Roman\',\'serif\';\">This paper explores the neural network as an eye disease classifier. In this investigation, the sets of parameters describing EEG data for eye states are taken. Thus the classification of eye status represented by the data sets becomes possible. In this multi layer neural network and principal component based performance analysis is explored. Selection of the optimal parameters such as number of hidden layers, learning rules and transfer functions are taken into consideration. The classification results are obtained through rigorous experimentation. Diabetic retinopathy is an eye syndrome caused by the impediment of diabetes and it can be detected prior for effective treatment. The vision of patient may start to deteriorate as diabetes progresses and lead to diabetic retinopathy. In this paper to establish diabetic retinopathy, two models like multi layer perception (MLP), Principal Component Analysis (PCA) are explained and their performances are compared. An automated approach for classification of the disease diabetic retinopathy using images is presented. Levenberg Marquart back propagation algorithm was implemented to train multilayer feed forward ANN. The designed classification structure has about 99 % sensitivity, 100 % specifity and correct classification is calculated to be 99%. The performances are classified as normal and diseased. Testing grades were found to be complaint with the accepted results that are imitative from the physician&rsquo;s direct diagnosis. The states results verify that the proposed method could point out the capability of design of a new intelligent assistance diagnosis system. An ANN is generally robust, tolerant of faults and noise, able to generalize well and capable of solving nonlinear problems by virtue of its parallel distribution. Artificial Neural network architecture can be trained to classify eye disease according to patient complaints, symptoms and physical eye examination, then decision trees to extract knowledge from trained neural networks in order to understand the knowledge represented by the trained network.</span></p>
 
<p class=\"MsoNormal\" style=\"text-align: justify; text-justify: inter-ideograph;\"><strong><em><span style=\"font-size: 10.0pt; line-height: 115%; font-family: \'Times New Roman\',\'serif\';\">Keywords</span></em></strong><span style=\"font-size: 12.0pt; line-height: 115%; font-family: \'Times New Roman\',\'serif\';\">: </span><strong><em><span style=\"font-size: 10.0pt; line-height: 115%; font-family: \'Times New Roman\',\'serif\';\">Eye Disease Classification, Multi-Layer Neural Network, Principal Component Analysis, Artificial Neural Network, Sensitivity, Specificity, Accuracy</span></em></strong></p>