基于BP神经网络的肝癌放疗致乙型肝炎病毒再激活分类预测模型

Classification prognosis model of hepatitis B virus reactivation after radiotherapy in patients with primary liver carcinoma based on BP neural network

研究针对原发性肝癌(primary liver carcinoma,PLC)患者精确放疗后乙型肝炎病毒(Hepatitis B virus,HBV)再激活分类预测模型,采用logistic提取关键特征子集,发现外放边界、肿瘤分期TNM和HBV DNA水平是HBV再激活的危险因素(P<0.05)。建立BP神经网络分类预测模型,对原发性肝癌初始数据集和关键特征子集进行HBV再激活分类预测。实验结果表明,BP网络对HBV再激活有着良好的分类预测性能,分类预测准确性从73.33%提高到78.89%,关键特征子集分类预测准确性高于初始数据集分类预测准确性,表明了特征提取后的关键特征子集具有优秀的类别区分性。

This study aims at the classification prognosis model of hepatitis B virus（ HBV） reactivation after precise radiotherapy（ RT）in patients with primary liver carcinoma. Using logistic regression methods to extract the key feature subset. Finding the outer margin of RT,TNM of tumor stage and HBV DNA level were the risk factors（ P〈 0.05） of HBV reactivation. Build classification prognosis model of BP neural network,classification prognosis of HBV reactivation for original data subset and the key feature subset in patients with primary liver carcinoma. Experimental results show that BP neural network has a good classification prognosis performance of HBV reactivation,the classification prognosis accuracy increased from 73. 33% to 78. 89%. The classification prognosis accuracy of the key feature subset is superior to original data subset,and the key feature subset has excellent classification of distinction after feature extraction.