CascadeTransformer: Multi-label Classification with Transformer in Chronic Disease Prediction

Abstract

Chronic diseases are serious threats to human safety and major public health problems worldwide. Many chronic diseases tend to have co-morbidities. Most machine learning techniques nowadays tend to focus on predicting a single disease while ignoring the study of co-morbidities. It is urgent to develop an artificial intelligence-based multi-label classification model based on patients' physical data, which is useful for the early detection and treatment of patients' diseases. In this study, we proposed a layer-by-layer processing structure, termed CascadeTransformer, that applies the Transformer architecture as weak classifiers, to solve the multi-label prediction problem of chronic diseases. We built a chronic diseases dataset using real-world data from West China Hospital, which consists of 1174 anonymous instances and 131 features. Systematic experiments show that our method shows better experimental performance compared to other methods on our chronic disease dataset.