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There are two fundamental problems in applying deep learning/machine learningmethods to disease classification tasks, one is the insufficient number andpoor quality of training samples; another one is how to effectively fusemultiple source features and thus train robust classification models. Toaddress these problems, inspired by the process of human learning knowledge, wepropose the Feature-aware Fusion Correlation Neural Network (FaFCNN), whichintroduces a feature-aware interaction module and a feature alignment modulebased on domain adversarial learning. This is a general framework for diseaseclassification, and FaFCNN improves the way existing methods obtain samplecorrelation features. The experimental results show that training usingaugmented features obtained by pre-training gradient boosting decision treeyields more performance gains than random-forest based methods. On thelow-quality dataset with a large amount of missing data in our setup, FaFCNNobtains a consistently optimal performance compared to competitive baselines.In addition, extensive experiments demonstrate the robustness of the proposedmethod and the effectiveness of each component of the model\footnote{Acceptedin IEEE SMC2023}.

FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks