论文标题
通过歧视性神经网络分析冠心病的风险
Analysing Risk of Coronary Heart Disease through Discriminative Neural Networks
论文作者
论文摘要
数据挖掘，机器学习和人工智能技术在诊断领域的应用并不是一个新概念，并且这些技术已经非常成功地应用于各种应用中，尤其是在皮肤病学和癌症研究中。但是，在涉及导致真或错误（二进制分类）测试的医学问题的情况下，数据通常具有主要属于一个类别的样本的类不平衡（例如：患者进行常规测试，结果是错误的）。当试图在数据上建模预测系统时，数据中的这种差异会引起问题。在诸如诊断之类的关键应用程序中，该类别的不平衡不能被忽略，必须给予额外的关注。在我们的研究中，我们描述了如何使用歧视模型和使用暹罗神经网络结构来通过神经网络来处理这一类失衡。这样的模型不适用于将样本分类为标签的基于概率的方法。相反，它使用基于距离的方法来区分不同标签下分类的样本。该代码可在https://tinyurl.com/discriminativechd/上获得
The application of data mining, machine learning and artificial intelligence techniques in the field of diagnostics is not a new concept, and these techniques have been very successfully applied in a variety of applications, especially in dermatology and cancer research. But, in the case of medical problems that involve tests resulting in true or false (binary classification), the data generally has a class imbalance with samples majorly belonging to one class (ex: a patient undergoes a regular test and the results are false). Such disparity in data causes problems when trying to model predictive systems on the data. In critical applications like diagnostics, this class imbalance cannot be overlooked and must be given extra attention. In our research, we depict how we can handle this class imbalance through neural networks using a discriminative model and contrastive loss using a Siamese neural network structure. Such a model does not work on a probability-based approach to classify samples into labels. Instead it uses a distance-based approach to differentiate between samples classified under different labels. The code is available at https://tinyurl.com/DiscriminativeCHD/
