Metabolic syndrome (MetS) has a high prevalence and is prone to many complications. However, current MetS diagnostic methods require blood tests that are not conducive to self-testing, so a user-friendly and accurate method for predicting MetS is needed to facilitate early detection and treatment. In this study, a MetS prediction model based on a simple, small number of Traditional Chinese Medicine (TCM) clinical indicators and biological indicators combined with machine learning algorithms is investigated. Electronic medical record data from 2040 patients who visited outpatient clinics at Guangdong Chinese medicine hospitals from 2020 to 2021 were used to investigate the fusion of Bayesian optimization (BO) and eXtreme gradient boosting (XGBoost) in order to create a BO-XGBoost model for screening nineteen key features in three categories: individual bio-information, TCM indicators, and TCM habits that influence MetS prediction. Subsequently, the predictive diagnostic model for MetS was developed. The experimental results revealed that the model proposed in this paper achieved values of 93.35 %, 90.67 %, 80.40 %, and 0.920 for the F1, sensitivity, FRS, and AUC metrics, respectively. These values outperformed those of the seven other tested machine learning models. Finally, this study developed an intelligent prediction application for MetS based on the proposed model, which can be utilized by ordinary users to perform self-diagnosis through a web-based questionnaire, thereby accomplishing the objective of early detection and intervention for MetS.

This study proposes Combining Attention and Brightness Adjustment Network (CABA-Net), a deep learning network for underwater image restoration, to address the issues of underwater image color-cast, low brightness, and low contrast. The proposed approach achieves a multi-branch ambient light estimation by extracting the features of different levels of underwater images to achieve accurate estimates of the ambient light. Additionally, an encoder-decoder transmission map estimation module is designed to combine spatial attention structures that can extract the different layers of underwater images’ spatial features to achieve accurate transmission map estimates. Then, the transmission map and precisely predicted ambient light were included in the underwater image formation model to achieve a preliminary restoration of underwater images. HSV brightness adjustment was conducted by combining the channel and spatial attention to the initial underwater image to complete the final underwater image restoration. Experimental results on the Underwater Image Enhancement Benchmark (UIEB) and Real-world Underwater Image Enhancement (RUIE) datasets show excellent performance of the proposed method in subjective comparisons and objective assessments. Furthermore, several ablation studies are conducted to understand the effect of each network component and prove the effectiveness of the suggested approach.