改进的属性约简算法及其在肝癌微血管侵犯预测中的应用

基于邻域粗糙集的属性约简算法在进行属性约简时只考虑单一属性对决策属性的影响，未能考虑各属性间的相关性，针对这个问题，提出了一种基于卡方检验的邻域粗糙集属性约简算法（ChiS-NRS）。首先，利用卡方检验计算相关性，在筛选重要属性时考虑相关属性之间的影响，在降低时间复杂度的同时提高了分类准确率；然后，将改进的算法与梯度提升决策树（GBDT）算法组合以建立分类模型，并在UCI数据集上对模型进行验证；最后，将该模型应用于预测肝癌微血管侵犯的发生。实验结果表明，与未约简、邻域粗糙集约简等几种约简算法相比，改进算法在一些UCI数据集上的分类准确率最高；在肝癌微血管侵犯预测中，与卷积神经网络（CNN）、支持向量机（SVM）、随机森林（RF）等预测模型相比，提出的模型在测试集上的预测准确率达到了88.13%，其灵敏度、特异度和受试者操作曲线（ROC）的曲线下面积（AUC）分别为87.10%、89.29%和0.90，各指标都达到了最好。因此，所提模型能更好地预测肝癌微血管侵犯的发生，能辅助医生进行更精确的诊断。

Improved attribute reduction algorithm and its application to prediction of microvascular invasion in hepatocellular carcinoma

Focused on the issue that the attribute reduction algorithm based on neighborhood rough set only considers the influence of a single attribute on the decision attribute, and fails to consider the correlation among different attributes, a Neighborhood Rough Set attribute reduction algorithm based on Chi-square test (ChiS-NRS) was proposed. Firstly, the Chi-square test was used to calculate the correlation, and the influence between the related attributes was considered when selecting the important attributes, making the time complexity reduced and the classification accuracy improved. Then, the improved algorithm and the Gradient Boosting Decision Tree (GBDT) algorithm were combined to establish a classification model and the model was verified on UCI datasets. Finally, the proposed model was applied to predict the occurrence of microvascular invasion in hepatocellular carcinoma. The experimental results show that the proposed algorithm has the highest classification accuracy on some UCI datasets compared with the reduction algorithm without reduction and neighborhood rough set reduction algorithm. In the prediction of microvascular invasion in hepatocellular carcinoma, compared with Convolution Neural Network (CNN), Support Vector Machine (SVM) and Random Forest (RF) prediction models, the proposed model has the prediction accuracy of 88.13% in test set, the sensitivity, specificity and the Area Under Curve (AUC) of Receiver Operating Curve (ROC) of 88.89%, 87.5% and 0.90 respectively are the best. Therefore, the prediction model proposed can better predict the occurrence of microvascular invasion in hepatocellular carcinoma and assist doctors to make more accurate diagnosis.