Fuzzy decision tree for breast cancer prediction

Mylene J. Domingo, B. Gerardo, Ruji P. Medina
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引用次数: 6

Abstract

Medical errors are considered as the leading cause of death and injury. Breast cancer becomes one of the leading causes of death among women, not only in the Philippines but worldwide. In this paper, data mining was used to predict the stage of breast cancer using a hybrid of fuzzy logic and decision tree. This aims to help experts to make decisions rather than replacing them. The result will only give an expert a recommendation, but the final decision is still on the hands of the experts. Feature selection was used to determine the best attribute in the dataset from Surveillance Epidemiology and End Results (SEER). The data set consists of incidence from 1975 to 2016, but the study limits the analysis from 2010 to 2016. Different cleaning and preprocessing of data are conducted. After thorough preprocessing of data, six (6) attributes are selected, and one (1) target class. Performance comparison shows that the fuzzy decision tree achieved a higher accuracy of 99.96%, sensitivity of 99.26% and specificity of 99.98% than the decision tree classification technique. The simulation result shows a correctly classified instance of 165,124, which is equivalent to 99.97% and only 351 incorrect classified instances or 0.21%. Thus, a fuzzy decision tree is more robust than the traditional decision tree classifier for predicting the stage of breast cancer.
模糊决策树用于乳腺癌预测
医疗事故被认为是造成死亡和伤害的主要原因。乳腺癌已成为菲律宾乃至全世界妇女死亡的主要原因之一。本文采用模糊逻辑和决策树相结合的方法,将数据挖掘技术应用于乳腺癌分期预测。其目的是帮助专家做出决策,而不是取代他们。结果只会给专家一个建议,但最终的决定权仍然掌握在专家手中。特征选择用于从监测流行病学和最终结果(SEER)中确定数据集中的最佳属性。该数据集包括1975年至2016年的发病率,但该研究限制了2010年至2016年的分析。对数据进行不同的清洗和预处理。经过对数据的深入预处理,选择了6个属性,1个目标类。性能比较表明,模糊决策树的准确率为99.96%,灵敏度为99.26%,特异性为99.98%,优于决策树分类技术。仿真结果表明,正确分类实例为165,124个,相当于99.97%,错误分类实例仅为351个,相当于0.21%。因此,模糊决策树比传统的决策树分类器在预测乳腺癌分期方面具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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