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Pattern Classification of Retinitis Pigmentosa Data for Prediction of Prognosis

망막색소변성 데이터의 예후 예측을 위한 패턴 분류

  • Received : 2011.09.15
  • Accepted : 2012.03.18
  • Published : 2012.06.30
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Abstract

Retinitis Pigmentosa(RP) is a common hereditary disease. While they have been normally living, those who have this symptom feel frustration and pain by the damage of visual acuity. At the national level, the loss of the economic activity due to the reduction of economically active population will be also greater. There is an urgent need for the base study that can provide the clinical prognosis information of RP disease. In this study, we suggest that it is possible to predict prognosis through the pattern classification of RP data. Statistical processing results through statistical software like SPSS(Statistical Package for the Social Service) were mainly applied for the conventional study in data analysis. However, machine learning and automatic pattern classification was applied to this study. SVM(Support Vector Machine) and other various pattern classifiers were used for it. The proposed method confirmed the possibility of prognostic prediction based on the result of automatically classified RP data by SVM classifier.

망막색소변성(RP: Retinitis Pigmentosa)이란 가장 흔한 유전성 망막질환이다. 정상적인 사회활동을 영위하던 사람들이 이 질병으로 시력이 손상되면서 좌절과 고통을 겪는다. 또한 국가적 차원에서 이들의 경제활동이 끊김에 따라 경제활동 인구 감소에 따른 손실 또한 크다고 하겠다. 이에 망막색소변성 질환에 대한 임상 예후 정보를 제공할 수 있는 연구기반이 절실히 요구되고 있다. 본 연구는 망막색소변성 데이터에 대한 패턴 분류를 통해 예후 예측이 가능함을 제안한다. 기존에는 주로 SPSS등을 활용한 통계 처리 결과가 데이터 분석에 적용되었다. 그러나 본 연구에서는 기계학습과 자동 패턴 분류를 실험하였다. SVM(Support Vector Machine)과 여러 다양한 패턴분류기들을 실험을 위해 사용하였다. 제안한 방법은 SVM 분류기에 의하여 RP 데이터가 자동적으로 분류된 결과를 바탕으로 예후 예측이 가능함을 확인하였다.

Keywords

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