Cloud Based Low Cost Retinal Detachment Screening Method Using Data Mining Techniques

The Electro-Oculogram (EOG) signal can be used for detecting the normality of the eye retina.  A number of advantages including the flexibility of the recoding process for EOG signals have encouraged insight into EOG based research. This study proposes a new cloud based retinal detachment screening technique based on data mining techniques to diagnose the type of eye retina. The used recognition methods include: back propagation neural network, Kohonen neural network and support vector machine. The obtained results are classified as normal or abnormal eye retina. Given a training set of such patterns, the proposed system is trained how to differentiate a new case in the domain. The diagnosis performance of the proposed systems is evaluated using more than one performance measure such as statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the proposed neural network has achieved a remarkable performance with 100% accuracy on training and test subsets. The main advantage of the proposed system is the high quality of the diagnosis process that help health team to take the suitable decision regarding the patient case. The proposed system may help reducing the cost of screening patients especially in rural areas where experts are not available through sending their data to a central site where the automatic system will help an expert to diagnose the suspected cases.