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A Comparison of Artificial Neural Network and Decision Trees with Logistic Regression as Classification Models for Breast Cancer Survival
International Journal of Mathematical, Engineering and Management Sciences Pub Date : 2020-12-01 , DOI: 10.33889/ijmems.2020.5.6.089
Venkateswara Rao Mudunuru , Leslaw A. Skrzypek

In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values. KeywordsArtificial neural networks, Logistic Regression, Breast cancer, Decision Trees, Cancer survival.

中文翻译:

人工神经网络与决策树的逻辑回归作为乳腺癌生存分类模型的比较

在医学领域,最近的一些研究表明,人工神经网络,决策树,逻辑回归的价值在预测器和分类方法中起着重要作用。这项研究已经扩大到估计乳腺癌,肺癌,肝癌,卵巢癌,宫颈癌,膀胱癌和皮肤癌的发病率。本文的主要目的是使用相同的输入和输出变量开发logistic回归,人工神经网络和决策树模型,并比较它们在预测女性乳腺癌生存中的成功率。为了找到最佳的乳腺癌生存模型,对所有这些模型的敏感性和特异性进行了测量,并用它们各自的置信区间和ROC值进行了评估。人工神经网络Logistic回归乳腺癌决策树
更新日期:2020-12-01
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