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Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

机译:对数逻辑回归和人工神经网络预测乳腺癌复发性能的比较

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Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.
机译:背景:乳腺癌是女性群体中最常见的癌症。确切原因尚不清楚,但最有可能是遗传因素和环境因素的组合。数逻辑模型(LLM)被应用作为一种统计方法用于预测生存和它影响因素。近几十年来,人工神经网络(ANN)模型已被越来越多地应用于预测生存数据。本研究是进行比较日志logistic回归和人工神经网络模型中的乳腺癌(BC)生存的预测。材料与方法:历史队列研究与104例患者患公元前1997年至2005年为比较ANN和LLM在我们的设置建立,我们使用接收机操作特性(ROC)曲线(AUC)下的估计和地区集成AUC(的iAUC)。使用R统计软件对数据进行分析。结果:AUC为第一,第二和第三年诊断后是0.918,分别0.780和0.800 ANN,和0.834,0.733和0.616 LLM,。对ANN的平均AUC在统计学上比LLM(0.845 0.744对比)高。因此,这项研究显示,通过神经网络和LLM预测方面的性能之间的差异显著。结论:这项研究表明,与ANN的预测能力比与LLM模型更高。因此,使用ANN方法为在乳腺癌的场存活预测的建议。

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