There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models. To address these problems, inspired by the process of human learning knowledge, we propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which introduces a feature-aware interaction module and a feature alignment module based on domain adversarial learning. This is a general framework for disease classification, and FaFCNN improves the way existing methods obtain sample correlation features. The experimental results show that training using augmented features obtained by pre-training gradient boosting decision tree yields more performance gains than random-forest based methods. On the low-quality dataset with a large amount of missing data in our setup, FaFCNN obtains a consistently optimal performance compared to competitive baselines. In addition, extensive experiments demonstrate the robustness of the proposed method and the effectiveness of each component of the model\footnote{Accepted in IEEE SMC2023}.

翻译：在将深度学习/机器学习方法应用于疾病分类任务时存在两个基本问题：一是训练样本数量不足且质量较差；二是如何有效融合多源特征从而训练鲁棒的分类模型。针对这些问题，受人类知识学习过程的启发，我们提出了特征感知融合相关神经网络（FaFCNN），该网络引入了特征感知交互模块和基于领域对抗学习的特征对齐模块。这是一个用于疾病分类的通用框架，FaFCNN改进了现有方法获取样本相关特征的方式。实验结果表明，使用通过预训练梯度提升决策树获得的增强特征进行训练，比基于随机森林的方法能获得更多性能提升。在我们设置的大量缺失数据的低质量数据集上，FaFCNN相较于竞争基线方法始终获得最优性能。此外，大量实验证明了所提方法的鲁棒性以及模型各组件的有效性。