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.

中文翻译：

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基于密集连接的深度神经网络的肝病筛查。

肝病是重要的公共卫生问题。肝功能检查（LFT）是诊断肝病最可行的检查。大多数肝脏疾病表现为LFT异常。通过LFT数据筛查肝脏疾病有助于计算机辅助诊断。在本文中，我们针对13种最常用的LFT指标和用于肝病筛查的受试者的人口统计学信息，提出了一个紧密连接的深度神经网络（DenseDNN）。该算法在76,914个样本的数据集上进行了测试（数据量是以前数据集的100倍以上）。DenseDNN的曲线下面积（AUC）为0.8919，DNN的为0.8867，随机森林的为0.8790，逻辑回归的为0.7974。深度学习模型的性能明显优于传统方法。至于深度学习方法，