The Early Stage Lung Cancer Prognosis Prediction Model based on Support Vector Machine

机译：基于支持向量机的早期肺癌预后预测模型

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摘要

According to the annual statistics of the American Cancer Society, lung cancer has become the leading cause of death for cancer patients. It is therefore vital to research lung cancer prognosis prediction model. From the characteristics of cancer data samples, we consider the unbalanced category data. Due to the small number of samples, one of the commonly used over-sampling techniques is selected, which is an improved Synthetic Minority Over-sampling Technique (Borderline-SMOTE) to expand a few types of samples. For labeling the dataset by 5-year survival time, support vector machines (SVM) and Cox-proportional hazard regression model (COX) were used for training and calculating, respectively. The results show that the performance of the proposed prognosis model based on SVM is better. Similarly, 2-year survival time as the standard for labeling the dataset, the experimental results also show that the performance of the proposed model is better, which verifies the validity and reliability of the designed model.

机译：根据美国癌症协会的年度统计，肺癌已成为癌症患者死亡的主要原因。因此，研究肺癌的预后预测模型至关重要。从癌症数据样本的特征，我们考虑不平衡类别数据。由于样本数量少，因此选择了一种常用的过采样技术，这是一种改进的合成少数采样技术（Borderline-SMOTE），可以扩展几种类型的样本。为了用5年生存时间标记数据集，分别使用了支持向量机（SVM）和Cox比例风险回归模型（COX）进行训练和计算。结果表明，所提出的基于支持向量机的预测模型的性能较好。同样，以2年生存时间为标记数据集的标准，实验结果还表明所提出模型的性能更好，验证了所设计模型的有效性和可靠性。

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来源
《2018 IEEE 23rd International Conference on Digital Signal Processing》|2018年|1-4|共4页
会议地点 Shanghai(CN)
作者
Zhuqing Cai; Zhuliang Yu; Haiyu Zhou; Zhenghui Gu;
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作者单位

School of Automation Science and Engineering, South China University of Technology, Guangzhou, China;

School of Automation Science and Engineering, South China University of Technology, Guangzhou, China;

Department of Thoracic Surgery, Guangdong General Hospital, Guangzhou, China;

School of Automation Science and Engineering, South China University of Technology, Guangzhou, China;

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原文格式 PDF
正文语种 eng
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关键词
Cancer; Support vector machines; Prognostics and health management; Lung; Sensitivity; Labeling; Indexes;

机译：癌症;支持向量机;预测和健康管理;肺;敏感性;标签;指标;;

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3. Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. [J] . Jayasurya K, Fung G, Yu S, Medical Physics . 2010,第4期

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The Early Stage Lung Cancer Prognosis Prediction Model based on Support Vector Machine

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