Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis
Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis
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DOI：
10.5812/ijcm.9176
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发表时间：
2018-07-01
影响因子：
0.7
通讯作者：
Akbari, Mohammad Esmaeil
中科院分区：
文献类型：
--
作者：
Momenyan, Somayeh;Baghestani, Ahmad Reza;Akbari, Mohammad Esmaeil
背景资料：乳腺癌是第一个原因，癌症相关的死亡妇女在Irana.Objectives：本研究的目的是比较传统的统计分析和数据挖掘技术的研究方法，用于确定预后因素的乳腺癌患者的生存时间。决策树方法是应用于医学领域的预测模型之一。最常用的算法是分类和回归树（CART），快速，无偏，高效的统计树（QUEST），卡方自动交互检测器（CHAIDS）算法，和C5.0 algorithm.Methods：我们使用的数据为438例患者，谁被称为癌症研究中心在沙希德Beheshti医科大学。这些患者于1992年至2012年间接受就诊和治疗，并随访至2014年10月。采用Logistic回归和决策树方法对数据进行分析。结果：C5.0算法在预测乳腺癌生存率方面优于CHAID、QUEST、CART算法和Logistic回归。多因素Logistic回归分析结果显示，年龄、病理分级、腋窝淋巴结状况、手术方式对乳腺癌患者的死亡率有统计学意义。此外，基于C4.5，他们报告说，肿瘤大小，初潮年龄，激素治疗，腋窝淋巴结的状态，和组织学分级是最突出的variable.Conclusions：更精确的方法可以确定更准确的预测。与传统的Logistic回归相比，决策树方法能够更准确地预测死亡概率。为了获得更好的预测性能，对经典分类树进行了一些改进，如Boosting和Bagging。我们建议，现代分类树方法在乳腺癌的背景下，是未来研究的重点。
Background: Breast cancer is the first cause of cancer-related deaths among women in Iran.Objectives: The aim of the present study was to compare the traditional statistical analysis and data mining technique as the research methods for identifying the prognostic factors regarding the survival time of patients with breast cancer. Decision tree method is one of the predictive models that used in the medical field. The most used algorithms are classification and regression trees (CART), the quick, unbiased, efficient statistical tree (QUEST), Chi-square automatic interaction detector (CHAIDs) algorithm, and the C5.0 algorithm.Methods: We used data for 438 patients, who were referred to cancer research center in Shahid Beheshti University of Medical Sciences. The patients were visited and treated during 1992 to 2012 and followed up until October 2014. The data were analyzed by regression logistic and decision tree method. Six measures for evaluation of predictive performance of different models were used.Results: The C5.0 algorithm performed better than CHAID, QUEST, CART algorithms, and the logistic regression in predicting breast cancer survival. The multiple logistic regression results indicated that the factors of age at diagnosis, histologic grade, axillary lymph node status, and type of surgery were statistically significant with regard to the probability of death in patients with breast cancer. Moreover, based on C4.5 they reported that tumor size, age of menarche, hormonal therapy, axillary nodal status, and histological grade are the most prominent variables.Conclusions: The more precise methods can identify the more accurate predictors. The decision tree method was able to predict the probability of death more accurately compared with the conventional logistic regression. Some improvements for classical classification tree such as boosting and bagging have been developed in order to obtain better predictive performance. We suggest that the modern classification tree method in the breast cancer context be the focus of future studies.