The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer
Journal of Electrical and Electronic Engineering
Volume 4, Issue 2, April 2016, Pages: 31-34
Received: Apr. 6, 2016; Published: Apr. 7, 2016
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Authors
Wang Shuai, School of Information, Qilu University of Technology, Jinan, China
Wu Guan-peng, School of Information, Qilu University of Technology, Jinan, China
Huang Wei, Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Liu Tong-hai, Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Yin Yong, Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Liu Yi-hui, School of Information, Qilu University of Technology, Jinan, China
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Abstract
In this paper, to build a predictive model of hepatitis B virus (HBV) reactivation in primary liver cancer (PLC) patients after precise radiotherapy (RT). Logistic regression analysis was adopted to extract the optimal feature subset, TNM, HBV DNA level and outer margin of RT were risk factors for HBV reactivation (P < 0.05). A predictive model of support vector machine (SVM) was established for the optimal feature subset and all of PLC data sets. The experimental results proved that the former obviously improves the classification accuracy, which increased from 74.44% to 78.89%. In this paper, it is concluded that TNM, HBV DNA levels and outer boundary are the risk factor for HBV reactivation (P < 0.05).
Keywords
Primary Liver Cancer, Data Set, Feature Extraction, Support Vector Machine (SVM)
To cite this article
Wang Shuai, Wu Guan-peng, Huang Wei, Liu Tong-hai, Yin Yong, Liu Yi-hui, The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer, Journal of Electrical and Electronic Engineering. Vol. 4, No. 2, 2016, pp. 31-34. doi: 10.11648/j.jeee.20160402.15
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