[1]刘 芬,刘秀峰.基于SMOTEboost的大肠癌不平衡数据集的症型分类研究[J].医学信息,2018,31(23):10-12.[doi:10.3969/j.issn.1006-1959.2018.23.003]
　LIU Fen,LIU Xiu-feng.Symptomatic Classification of Colorectal Cancer Unbalanced Data Set Based on SMOTEboost[J].Journal of Medical Information,2018,31(23):10-12.[doi:10.3969/j.issn.1006-1959.2018.23.003]
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基于SMOTEboost的大肠癌不平衡数据集的症型分类研究()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
31卷
期数:
2018年23期
页码:
10-12
栏目:
出版日期:
2018-12-01

文章信息/Info

Title:
Symptomatic Classification of Colorectal Cancer Unbalanced Data Set Based on SMOTEboost
文章编号:
1006-1959（2018）23-0010-03
作者:
刘 芬; 刘秀峰
广州中医药大学，广东 广州 510006
Author(s):
LIU Fen; LIU Xiu-feng
Guangzhou University of Traditional Chinese Medicine,Guangzhou 510006,Guangdong,China
关键词:
大肠癌; Adaboost; SMOTE; 不平衡数据集
Keywords:
Colorectal cancer; Adaboost; SMOTE; Unbalanced dataset
分类号:
TP311.13；R735.3+4
DOI:
10.3969/j.issn.1006-1959.2018.23.003
文献标志码:
A
摘要:
利用机器学习算法处理大肠癌中医症状与证型数据，建立适用于大肠癌不平衡数据集的虚实证型分类模型。方法 量化收集的大肠癌中医临床病案数据以及文献检索数据，首先采用人工合成过采样技术对数据集进行平衡处理，再结合集成学习算法Adaboost，将症状作为输入，证型作为输出，对比传统机器学习算法分类效果，探讨大肠癌的SMOTEboost分类模型在虚实证型中的分类性能。结果 SMOTEboost分类算法在大肠癌虚实证型分类模型中，F-mean较Adaboost提高了25.40%，G-mean提高了16.60%，表明构建的分类器具有较高的分类性能与泛化能力。结论 SMOTEboost复合分类器为大肠癌中医证型分类提供很好的计算机网络模型，也为其他中医病症的临床证型判定提供借鉴意义。
Abstract:
Objective To establish a classification model of deficiency and solid syndrome type suitable for unbalanced data set of colorectal cancer by using machine learning algorithm to process TCM symptom and syndrome type data of colorectal cancer.Methods The data of TCM clinical records and literature retrieval of colorectal cancer were collected quantitatively.First, the artificial oversampling technique is used to balance the data set, and then the integrated learning algorithm Adaboost, is used to deal with the data set balance.Take symptoms as input, syndrome as output,and then compared the classification effect of traditional machine learning algorithm in order to study the classification performance of SMOTEboost classification model of colorectal cancer in deficiency and solid syndrome.Results In SMOTEboost classification algorithm used in the classification model of colonic carcinoma,F-mean increased 25.40% and G-mean by 16.60% compared with Adaboost which shows that the constructed classifier has high classification performance and generalization ability.Conclusion The SMOTEboost compound classifier can provide a good computer network model for the classification of TCM syndromes of colorectal cancer, and also provide reference for other TCM syndromes.

参考文献/References:

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更新日期/Last Update: 2018-12-17