Ophthalmological Examination Determination Using Data Classification Based on Support Vector Machines and Self-Organizing Maps

• KAMIURA Naotake

  Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, Japan

• SAITOH Ayumu

  Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, Japan

• ISOKAWA Teijiro

  Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, Japan

• MATSUI Nobuyuki

  Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, Japan

• TABUCHI Hitoshi

  Department of Ophthalmology, Tsukazaki Hospital, Japan

Abstract

In this paper, a method of determining examinations is presented for new outpatients visiting the department of ophthalmology, using support vector machines (SVM's) and self-organizing maps (SOM's). Assuming that interview sheets are divided into four classes, the proposed method copes with the examination determination as the classification of the sheets. The data are generated from handwriting sentences in the sheets, and they are arranged in the form of a matrix. Some nouns and adjectives in the sentences are chosen as elements of the matrix, and are assigned to columns of the matrix. The sentences in each sheet are assigned to a row of the matrix. The element values basically depend on values associated with frequencies of the chosen words appearing in the sentences. The proposed method uses rows as training data, and constructs a discrimination model, based either on normal SVM learning or on normal SOM learning. The SVM-based method defines four discriminant functions associated with the model. Since one-versus-all approach is employed, the class of data associated with the sheet to be examined is determined according to output values of the four functions. The SOM-based method labels neurons in the model (map) after normal learning is complete. The data class 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 an average ophthalmologist working at the leading hospital.

Journal

• Journal of Japan Society for Fuzzy Theory and Intelligent Informatics

  Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 26 (2), 559-572, 2014

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• support vector machines
• self-organizing maps
• examination determination
• data classification
• department of ophthalmology

Details

• CRID: 1390001205185596672
• NII Article ID: 130004934159
• DOI: 10.3156/jsoft.26.559
• ISSN: 18817203
• ISSN: 13477986
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed