ArticleFood & nutrition research2020
Prediction model of artificial neural network for the risk of hyperuricemia incorporating dietary risk factors in a Chinese adult study.
Article in Food & nutrition research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 23 citations in OpenAlex.
- Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network.BMC public health · 2021Trial
- Effects of coffee and tea consumption on hyperuricemia and gout: a systematic review and meta-analysis.Nutrition research and practice · 2025Review
- Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.BMC medical informatics and decision making · 2025Article
- Identifying Food Preferences and Malnutrition in Older Adults in Care Homes: Co-Design Study of a Digital Nutrition Assessment Tool.JMIR aging · 2025Article
- 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
- Investigating artificial intelligence models for predicting joint pain from serum biochemistry.Revista da Associacao Medica Brasileira (1992) · 2024Article
- Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network.Frontiers in oncology · 2023Article
- Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.International journal of environmental research and public health · 2022Article
- Review
- Association of eating out frequency and other factors with serum uric acid levels and hyperuricemia in Chinese population.European journal of nutrition · 2022Article
- Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients.Frontiers in public health · 2022Article
- Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4-7th Korea National Health and Nutrition Examination Survey.International journal of environmental research and public health · 2021Article
- Prediction model of random forest for the risk of hyperuricemia in a Chinese basic health checkup test.Bioscience reports · 2021Article
- Gender-Specific Inverse Associations Between Beans Intake, Serum Urate Levels, and Hyperuricemia: A Cross-Sectional Analysis Based on the Henan Rural Cohort Study.Frontiers in nutrition · 2020Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk of hyperuricemia (HU) has been shown to be strongly associated with dietary factors. However, there is scarce evidence on prediction models incorporating dietary factors to estimate the risk of HU.
objectiveThe aim of this study was to develop a prediction model to predict the risk of HU in Chinese adults based on dietary information.
designOur study was based on a cross-sectional survey, which recruited 1,488 community residents aged 18 to 60 years in Beijing from October 2010 to January 2011. The eligible participants were randomly divided into a training set (
resultsIn the training set, the mean age of participants with and without HU was 39.3 (standard deviation [SD]: 9.65) and 38.2 (SD: 9.38) years, respectively. Patients with HU consisted of 101 males (77.7%) and 29 females (22.3%). The LRM found that food frequency (vegetables [odds ratio (OR) = 0.73], meat [0.72], eggs [0.80], plant oil [0.78], tea [0.51], eating habits (breakfast [OR = 1.28]), and the salty cooking style (OR = 1.33) were associated with risk of HU. In the ANN analysis, we selected a three-layer back propagation neural network (BPNN) model with 14, 3, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The areas under the ROC of the training and validation sets were 0.827 and 0.814, respectively. HU would occur when the incidence probability is greater than 0.128. The indicators of accuracy, sensitivity, specificity, and Yuden Index suggested that the ANN model in our study is successful and valuable.
conclusionsThis study suggests that the ANN model could be used to predict the risk of HU in Chinese adults. Further prospective studies are needed to improve the accuracy and to generalize the use of model.
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