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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (2): 41-46    DOI: 10.11925/infotech.2096-3467.2017.02.06
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Building Asian Tumor-patients Prognostic Model with Bayesian Network and SEER Database——Case Study of Non-Small Cell Lung Cancer
Yin Bincan1, Xin Shichao1, Zhang Han1, Zhao Yuhong1,2()
1Department of Medical Informatics, China Medical University, Shenyang 110122, China
2Shengjing Hospital of China Medical University, Shenyang 110004, China
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Abstract  

[Objective] This study aims to improve the tumor-prognostic assessment for Asian patients who were diagnosed with Non-Small Cell Lung Cancer (NSCLC). The proposed model identifies the influencing factors of the patients’ survival status and predicts their prognostic situation. [Methods] First, we used single factor statistical method and logistic regression to identify the prognostic variables. Second, we employed the Bayesian Network algorithm to construct the prognostic survival model for the Asian NSCLC patients. Finally, we compared the performance of our model with three other algorithms. [Results] The identified prognostic variables include age, tumor size, grade, tumor stage, as well as the lymph nodes ratio. The proposed model could predict NSCLC patients’ prognostic survival status effectively. [Limitations] The SEER database had limited number of prognostic factors, which may influence the prediction accuracy. [Conclusions] The Bayesian Network could help us build optimal prognosis model for cancer patients to improve their survival rates. The proposed model is better than the Decision Tree, Support Vector Machine and Artificial Neural Network models.

Key wordsBayesian Networks      Non-Small Cell Lung Cancer      Prognosis      Machine Learning     
Received: 31 October 2016      Published: 27 March 2017
ZTFLH:  R730.7 G35  

Cite this article:

Yin Bincan,Xin Shichao,Zhang Han,Zhao Yuhong. Building Asian Tumor-patients Prognostic Model with Bayesian Network and SEER Database——Case Study of Non-Small Cell Lung Cancer. Data Analysis and Knowledge Discovery, 2017, 1(2): 41-46.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.02.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I2/41

数据类型 变量 SEER中所示名称 类数/数值范围
分类型 性别 Sex 2
国别 Race recode (Asian) 8
婚姻状况 Marital status at
diagnosis
4
发病部位 Primary Site - labeled 5
病理类型 ICD-O-3 Hist/behav,
malignant
4
组织学分级 Grade 4
患侧部位 Laterality 2
邻近器官
浸润程度
CS extension 18
区域淋巴结
累积程度
CS lymph nodes 5
远处转移程度 CS mets at dx 5
肿瘤分期 Derived AJCC
Stage Group
7
手术类型 RX Summ--Surg
Prim Site
13
是否放疗 Radiation 3
连续型 确诊时年龄 Age at diagnosis 26-90
肿瘤大小 CS tumor size 4-132
阳性淋巴结数量 Regional nodes
positive
0-23
受检淋巴结数量 Regional nodes
examined
1-45
变量名称 B S.E. Exp(B) 95% Exp(B) Sig.
下限 上限
确诊时年龄 -0.066 0.011 0.936 0.916 0.957 0.000
肿瘤大小 -0.018 0.007 0.982 0.968 0.996 0.014
组织学分级 / / / / / 0.001
肿瘤分期 / / / / / 0.013
受检淋巴结
数量
0.050 0.017 1.051 1.016 1.087 0.004
阳性淋巴结
数量
-0.199 0.067 0.819 0.719 0.934 0.003
所用分类算法 预测准确率
训练集 测试集
贝叶斯网络 0.683 0.729
决策树 0.713 0.670
支持向量机 0.733 0.686
人工神经网络 0.784 0.649
算法 预测准确率 精确度 ROC曲线下面积
贝叶斯网络 72.87% 71.0% 0.67
决策树 67.02% 66.3% 0.568
支持向量机 68.62% 68.2% 0.611
人工神经网络 64.89% 63.7% 0.615
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