ArticleBioscience reports2021
Prediction model of random forest for the risk of hyperuricemia in a Chinese basic health checkup test.
Article in Bioscience reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.BMC medical informatics and decision making · 2025Article
- Association between Visceral Adiposity Index and Hyperuricemia among Steelworkers: The Moderating Effects of Drinking Tea.Nutrients · 2024Article
- An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.Scientific reports · 2024Article
- Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population.Frontiers in physiology · 2024Article
- One-Year Risk Prediction of Elevated Serum Uric Acid Levels in Older Adults: A Longitudinal Cohort Study.Clinical interventions in aging · 2024Article
- Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset.International journal of environmental research and public health · 2023Article
- Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients.Frontiers in public health · 2022Article
- Prevalence of hyperuricemia and the population attributable fraction of modifiable risk factors: Evidence from a general population cohort in China.Frontiers in public health · 2022Article
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Authors and funding
16 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe present study aimed to develop a random forest (RF) based prediction model for hyperuricemia (HUA) and compare its performance with the conventional logistic regression (LR) model.
methodsThis cross-sectional study recruited 91,690 participants (14,032 with HUA, 77,658 without HUA). We constructed a RF-based prediction model in the training sets and evaluated it in the validation sets. Performance of the RF model was compared with the LR model by receiver operating characteristic (ROC) curve analysis.
resultsThe sensitivity and specificity of the RF models were 0.702 and 0.650 in males, 0.767 and 0.721 in females. The positive predictive value (PPV) and negative predictive value (NPV) were 0.372 and 0.881 in males, 0.159 and 0.978 in females. AUC of the RF models was 0.739 (0.728-0.750) in males and 0.818 (0.799-0.837) in females. AUC of the LR models were 0.730 (0.718-0.741) for males and 0.815 (0.795-0.835) for females. The predictive power of RF was slightly higher than that of LR, but was not statistically significant in females (Delong tests, P=0.0015 for males, P=0.5415 for females).
conclusionCompared with LR, the good performance in HUA status prediction and the tolerance of features associations or interactions showed great potential of RF in further application. A prospective cohort is necessary for HUA developing prediction. People with high risk factors should be encouraged to actively control to reduce the probability of developing HUA.
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