Classification of Interview Sheets Using Self-Organizing Maps for Determination of Ophthalmic Examinations

In this paper, a method of determining examinations is presented for outpatients visiting the department of ophthalmology. It assumes that each of the interview sheets belongs to one of the four classes, and copes with the examination determination as the classification of the sheets using self-organizing maps. Training data presented to the maps are generated from handwriting sentences in the sheets. Some nouns, adjectives and adverbs that ophthalmologists consider to be of comparative importance are chosen as elements of the training data. The element values basically depend on frequencies of the chosen words appearing in the sentences. After map learning is complete, neurons in the map are labeled. The data class associated with the sheet to be checked is given as the label of the winner neuron for the presented data. It is established that the proposed method achieves as favorable classification accuracy as initial determination made by ophthalmologists.