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基于SMOTE算法和机器学习模型建立原发性肝癌术后的预后预测模型
Construction of postoperative prognostic model for primary liver cancer based on SMOTE and machine learning
- 陆军军医大学学报 2024年46卷第19期 页码：2236-2240
- 作者机构：
400038 重庆，陆军军医大学(第三军医大学)军事预防医学系军队卫生统计学教研室
- 作者简介：
[李芳，E-mail: oucfli@163.com LI Fang, E-mail: oucfli@163.com
- 基金信息：国家自然科学基金项目(82173621);国家自然科学基金项目(81872716)
DOI：10.16016/j.2097-0927.202310052
中图分类号： R319;R730.7;R735.7收稿：2023-10-13，
修回：2024-07-02，
纸质出版：2024-10-15
- 稿件说明：
移动端阅览
潘比, 余靖华, 黄译贤, 等. 基于SMOTE算法和机器学习模型建立原发性肝癌术后的预后预测模型[J]. 陆军军医大学学报, 2024,46(19):2236-2240.
Bi PAN, Jinghua YU, Yixian HUANG, et al. Construction of postoperative prognostic model for primary liver cancer based on SMOTE and machine learning[J]. Journal of Army Medical University, 2024, 46(19): 2236-2240.
潘比, 余靖华, 黄译贤, 等. 基于SMOTE算法和机器学习模型建立原发性肝癌术后的预后预测模型[J]. 陆军军医大学学报, 2024,46(19):2236-2240. DOI： 10.16016/j.2097-0927.202310052.
Bi PAN, Jinghua YU, Yixian HUANG, et al. Construction of postoperative prognostic model for primary liver cancer based on SMOTE and machine learning[J]. Journal of Army Medical University, 2024, 46(19): 2236-2240. DOI： 10.16016/j.2097-0927.202310052.
摘要
目的
2
基于合成少数类过采样技术(synthetic minority over-sampling technique
SMOTE)算法和机器学习模型构建原发性肝癌术后的预后预测模型。
方法
2
选取美国国立癌症研究所的监测、流行病学及最终结果(Surveillance
Epidemiology
and End Results
SEER)数据库中4 297例患者进行回顾性队列研究，通过独热编码和平均值插补法进行数据预处理，利用SMOTE算法解决数据类别不平衡问题，将临床变量纳入机器学习模型，基于决策树(decision tree
DT)、随机森林(random forest
RF)、梯度提升决策树(gradient boosting decision tree
GBDT)、极限梯度提升算法(eXtreme Gradient Boosting
XGBoost)方法构建预后预测模型(SMOTE+DT/RF/GBDT/XGBoost)，通过比较多种模型的性能，筛选出最佳的预测模型。
结果
2
组合模型SMOTE+RF展示出最优的预测性能，受试者工作特征曲线(receiver operating characteristic curve
ROC)下的面积(area under the curve
AUC)、准确率和精确率均高于其他模型，分别为0.895、0.811、0.806。
结论
2
基于SMOTE+RF算法的原发性肝癌的预后预测模型可有效预测原发性肝癌患者的生存结局。
Abstract
Objective
2
To construct a prognosis prediction model of primary liver cancer after surgical treatment based on synthetic minority over-sampling technique(SMOTE) algorithm and machine learning model.
Methods
2
A retrospective cohort study was conducted on 4 297 patients with primary liver cancer from the surveillance
epidemiology
and end results(SEER) database. One-Hot Encoding and Multiple Imputation were used to preprocess the collect data
and SMOTE algorithm was employed to solve the imbalance of data categories. The obtained clinical variables were included in the machine learning model. Based on decision tree(DT)
random forest(RF)
gradient boosting decision tree(GBDT) and eXtreme Gradient Boosting(XGBoost)
a prognostic prediction model(SMOTE+DT/RF/GBDT/XGBoost) was build
and then the best prediction model was determined by comparing the performance of various models. Finally
a prognostic analysis system for primary liver cancer was developed based on the optimal model
which was then visualized.
Results
2
The combination model SMOTE+RF showed the best predictive performance
with higher area under the curve(0.895)
accuracy(0.811) and precision(0.806) than those of other models in receiver operating characteristic curve(ROC) analysis.
Conclusion
2
The SMOTE+RF prognostic prediction model can effectively predict the survival outcome of patients with primary liver cancer.
关键词
Keywords
references
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