A novel deep neural network model for multi-label chronic disease prediction

Abstract

Chronic diseases are one of the biggest threats to human life. It is clinically significant to predict the chronic disease prior to diagnosis time and take effective therapy as early as possible. In this work, we use problem transform methods to convert the chronic diseases prediction into a multi-label classification problem and propose a novel convolutional neural network (CNN) architecture named GroupNet to solve the multi-label chronic disease classification problem. Binary Relevance (BR) and Label Powerset (LP) methods are adopted to transform multiple chronic disease labels. We present the correlated loss as the loss function used in the GroupNet, which integrates the correlation coefficient between different diseases. The experiments are conducted on the physical examination datasets collected from a local medical center. In the experiments, we compare GroupNet with other methods and models. GroupNet outperforms others and achieves the best accuracy of 81.13%.