论文标题
机器学习算法的可解释性方法在乳腺癌诊断中应用
Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis
论文作者
论文摘要
乳腺癌的早期发现是减轻其社会经济负担的有力工具。尽管人工智能（AI）方法对此目标显示出了显着的结果，但他们的“黑匣子”性质阻碍了他们在临床实践中的广泛采用。为了满足对AI引导的乳腺癌诊断的需求，可以利用可解释性方法。在这项研究中，我们使用了AI方法，即随机森林（RF），神经网络（NN）和神经网络（ENN）的集合，以实现这一目标，并通过全球替代方法（GS）方法来解释和优化其性能，例如个人预期（ICE）（ICE）图（ICE）图（ICE）图（ICE）图（冰）图（shapley and shapley值（sv）。 Wisconsin诊断乳腺癌（WDBC）的数据集用于AI算法的培训和评估。拟议的ENN（96.6％的精度和ROC曲线下的0.96个面积）实现了乳腺癌诊断的最佳性能，其预测是通过冰块来解释的，证明其决策符合当前的医学知识，可进一步用于获得乳腺癌病理学机制的新见解。 Feature selection based on features' importance according to the GS model improved the performance of the RF (leading the accuracy from 96.49% to 97.18% and the area under the ROC curve from 0.96 to 0.97) and feature selection based on features' importance according to SV improved the performance of the NN (leading the accuracy from 94.6% to 95.53% and the area under the ROC curve from 0.94 to 0.95).与同一数据集上的其他方法相比，我们提出的模型在被解释的同时证明了最先进的性能状态。
Early detection of breast cancer is a powerful tool towards decreasing its socioeconomic burden. Although, artificial intelligence (AI) methods have shown remarkable results towards this goal, their "black box" nature hinders their wide adoption in clinical practice. To address the need for AI guided breast cancer diagnosis, interpretability methods can be utilized. In this study, we used AI methods, i.e., Random Forests (RF), Neural Networks (NN) and Ensembles of Neural Networks (ENN), towards this goal and explained and optimized their performance through interpretability techniques, such as the Global Surrogate (GS) method, the Individual Conditional Expectation (ICE) plots and the Shapley values (SV). The Wisconsin Diagnostic Breast Cancer (WDBC) dataset of the open UCI repository was used for the training and evaluation of the AI algorithms. The best performance for breast cancer diagnosis was achieved by the proposed ENN (96.6% accuracy and 0.96 area under the ROC curve), and its predictions were explained by ICE plots, proving that its decisions were compliant with current medical knowledge and can be further utilized to gain new insights in the pathophysiological mechanisms of breast cancer. Feature selection based on features' importance according to the GS model improved the performance of the RF (leading the accuracy from 96.49% to 97.18% and the area under the ROC curve from 0.96 to 0.97) and feature selection based on features' importance according to SV improved the performance of the NN (leading the accuracy from 94.6% to 95.53% and the area under the ROC curve from 0.94 to 0.95). Compared to other approaches on the same dataset, our proposed models demonstrated state of the art performance while being interpretable.
