Fatty liver disease (FLD) is a common liver disease, which poses a great threat to people's health, but there is still no optimal method that can be used on a large-scale screening. This research is based on machine learning algorithms, using electronic physical examination records in the health database as data support, to a predictive model for FLD. The model has shown good predictive ability on the test set, with its AUC reaching 0.89. Since there are a large number of electronic physical examination records in most of health database, this model might be used as a non-invasive diagnostic tool for FLD for large-scale screening.

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With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the scale of data continues to grow. The recommendation system has become one of the important intelligent software to help users make decisions. The recommendation model based on user rating data is widely studied and applied, but the data sparsity problem and the cold start problem seriously affect the recommendation quality. In this paper, Multi-view Hybrid Recommendation Model (MHRM) based on deep learning is proposed. First, we use WLDA (an improved Latent Dirichlet Allocation method) to extract the vector representation of user review text, and then apply LSTM to contextual semantic level user review sentiment analysis. At the same time, the emotion fusion method based on user score embedding is proposed. The problems such as deviations between the user score and actual interest preference, and unbalanced distribution of the score level are solved. This paper has been tested on Amazon product data and compared with various classic recommendation algorithms, using Mean Absolute Error (MAE), hit rate and standardized discount cumulative return for performance evaluation. The experimental results show that the prediction of the MHRM proposed in this paper on the 7 recommendation data and the TopN recommendation index have been significantly improved.