Objectives: The present study aimed to develop a random forest (RF) based prediction
model for hyperuricemia (HUA) and compare its performance with the conventional logistic …

This study aims to establish a simple and non-invasive risk prediction model for
hyperuricemia in Chinese adults based on modifiable risk factors. In 2020–2021, the …

Background Risk of hyperuricemia (HU) has been shown to be strongly associated with
dietary factors. However, there is scarce evidence on prediction models incorporating …

Background Patients with type 2 diabetes Mellitus (T2DM) are more likely to suffer from a
higher uric acid level in blood—hyperuricemia (HUA). There are no conclusive studies done …

Currently, the most widely used screening methods for hyperuricemia (HUA) involves
invasive laboratory tests, which are lacking in many rural hospitals in China. This study …

Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain
unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to …

Background: Hyperuricemia (HUA) contributes to gout and many other diseases. Many
hyperuricemia-related risk factors have been discovered, which provided the possibility for …

Background: Machine learning (ML) is a promising methodology for classification and
prediction applications in healthcare. However, this method has not been practically …

OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in
China after diabetes, and the disease burden is not optimistic. METHODS: We used the …

Objective To explore the associations between physiological factors, psychosocial factors,
dietary habits, lifestyles and hyperuricemia (HUA) and provide the evidence for …