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 …

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

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 …

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder
characterized by prolonged and asymptomatic period, triggering gout and metabolism …

Background: Hyperuricemia is a serious health problem related to not only gout but also
cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid …

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 …

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 …

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 …

Objective To develop a machine learning-based prediction model for identifying
hyperuricemic participants at risk of developing gout. Methods A retrospective nationwide …