Establishment and comparison of prediction models for early-stage diabetic kidney disease.

Background: The construction of a model to estimate patients' status in early-stage diabetic kidney disease (ES-DKD) is needed. Thus, the risk factors playing a role in the disease diagnosis can be determined when routine examination outcomes are collected. Objective: Routine examination outcomes can also be used to predict patients' ES-DKD. A first-stage study is conducted on how successful conventional statistical models (CSMs) perform when sample sizes are small when compared to machine learning methods (MLMs). Methods: A total of 268 observations were collected from two tertiary hospitals in Lanzhou with demographic information, basic medical history, and routine laboratory tests such as blood routine, common biochemical tests, and urine routine. Then, conventional statistical methods and MLMs are applied to establish models separately to determine optimal prediction models. In addition, machine learning has also been applied to establish fused models to explore new modeling methods. Results: The validation set can better represent the actual performance of the models in clinical practice. Therefore, the comparisons are made based on the predictive performance of the two methods using the validation set. Ultimately, it was concluded that the ensemble model outperforms in terms of performance metrics. The CSMs perform poorly in terms of area under curve values. Compared to various MLMs, the performance of others is not inferior. Conclusion: This article establishes multiple ES-DKD prediction models using CSMs and MLMs. New ideas and methods for the diagnosis, treatment, and prevention of ES-DKD in clinical practice are presented. This article also compares two modeling methods. A comprehensive model was established, which has excellent predictive and generalization ability and stability. Therefore, the integration of the advantages of MLMs based on CSMs is a very fruitful attempt. Fused models have a high chance of being the main research direction for future research to develop better models.