Ophthalmological Examination Determination Using Data Classification Based on Feedforward Neural Networks

In this paper, a method of determining examinations is proposed for ophthalmologic outpatients, using feedforward neural network (NN for short). The determination is based on the data classification. The proposed method defines four classes of ophthalmologic examinations. It prepares data for NN training and examination determination from handwriting sentences in outpatients' interview sheets. A set of the training data is prepared in the form of a matrix. The words extracted from the sentences are assigned to the matrix columns, while each sheet (or sentences in it) is assigned to a matrix rows. Entries in the matrix takes binary values meaning whether extracted words appear in the sentences in the sheet. The proposed method also the ages of outpatients as entries. NN training is conducted according to the normal backpropagation algorithm using a row as one of the training data. The trained NN has four output neurons each of which takes the value belonging to the range [0, 1]. The class of data to be examined is determined by searching the neuron at which the largest value appears in the output layer. Experimental results comprehensively establish that the proposed method can achieve higher percentages of concordance than other methods.