Risk Prediction of Renal Failure for Chronic Disease Population Based on Electronic Health Record Big Data

Abstract

Renal failure is a fatal disease raising global concerns. Previous risk models for renal failure mostly rely on the diagnosis of chronic kidney disease, which lacks obvious clinical symptoms and thus is mostly undiagnosed, causing significant omission of high-risk patients. In this paper, we proposed a framework to predict the risk of renal failure directly from a big data repository of chronic disease population without prerequisite diagnosis of chronic kidney disease. The electronic health records of 42,256 patients with hypertension or diabetes in Shenzhen Health Information Big Data Platform were collected, with 398 suffered from renal failure during a 3-year follow-up. Five state-of-the-art machine learning methods are utilized to build risk prediction models of renal failure for chronic disease population. Extensive experimental results show that the proposed framework achieves quite well performance. Particularly, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.9139. By analyzing the effect of risk factors, we identified that serum creatine, age, urine acid, systolic blood pressure, and blood urea nitrogen are the top five factors associated with renal failure risk. Compared with existing models, our model can be deployed into routine chronic disease management procedures and enable more preemptive, widely-covered screening of renal risks, which would in turn reduce the damage caused by the disease through timely intervention.

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