Liver disease screening based on densely connected deep neural networks

Liver disease is an important public health problem. Liver Function Tests (LFT) is the most achievable test for liver disease diagnosis. Most liver diseases are manifested as abnormal LFT. Liver disease screening by LFT data is helpful for computer aided diagnosis. In this paper, we propose a densely connected deep neural network (DenseDNN), on 13 most commonly used LFT indicators and demographic information of subjects for liver disease screening. The algorithm was tested on a dataset of 76,914 samples (more than 100 times of data than the previous datasets). The Area Under Curve (AUC) of DenseDNN is 0.8919, that of DNN is 0.8867, that of random forest is 0.8790, and that of logistic regression is 0.7974. The performance of deep learning models are significantly better than conventional methods. As for the deep learning methods, DenseDNN shows better performance than DNN.