統計的識別手法を用いたC型肝炎患者の血液検査データによる病態識別

抄録

Presently, doctors predict the condition of hepatitis C using blood examination data based on their professional experience, and patients are then diagnosed by performing a liver biopsy to obtain a definite diagnosis. However, liver biopsies are a high-risk procedure and can be troublesome. In this paper, we suggest a new method that is easier and more accurate. It uses the SVM (support vector machine), which is one of the most effective learning machines, and SFFS (sequential forward floating search), which is a feature selection. The combination of SVM and SFFS make it possible to eliminate the unnecessary examination of various items. It also helps to obtain high accuracy compared to using only SVM. Performance was drastically improved by applying our new method to the blood examination data for hepatitis C.