Environmental chemical exposure dynamics and machine learning-based prediction of diabetes mellitus
Background: With dramatically increasing prevalence, diabetes mellitus has imposed a tremendous toll on indi- vidual well-being. Humans are exposed to various environmental chemicals, which have been postulated as un- derappreciated but potentially modifiable diabetes risk factors. Objectives: To determine the utility of environmental chemical exposure in predicting diabetes mellitus. Methods: A total of 8501 eligible participants from NHANES 2005–2016 were randomly assigned to a discovery (N= 5953) set and a validation (N= 2548) set. We applied random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation in the discovery set to select features, and built an optimal model to predict diabetes mellitus, blood insulin, fasting plasma glucose (FPG) and 2-h plasma glucose after oral glucose tolerance test (2-h PG after OGTT). Results: The machine learning model using LASSO regression predicted diabetes with an area under the receiver operating characteristics (AUROC) of0.80 and 0.78 in the discovery set and validation set, respectively. The linear model predicted blood insulin level with an R2 of 0.42 and 0.40 in the discovery set and validation set, respec- tively. For FPG, the discovery set and validation set yielded an R2 of 0.16 and 0.15, respectively. For 2-h PG after OGTT, the discovery set and validation set yielded an R2 of 0.18 and 0.17, respectively. Conclusion: We used environmental chemical exposure, constructedmachine learning models and achieved rel- atively accurate prediction for diabetes, emphasizing the predictive value of widespread environmental chemicals for complicated diseases.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx c…
Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures
Environmental health researchers may aim to identify exposure patterns that represent sources, product use, or behaviors that give rise to mixtures of potentially harmful environmental chemical exposures. We present Baye…
Uncertainty QuantificationA network biology-based approach to evaluating the effect of environmental contaminants on human interactome and diseases
Environmental contaminant exposure can pose significant risks to human health. Therefore, evaluating the impact of this exposure is of great importance; however, it is often difficult because both the molecular mechanism…
The Biosensor based on electrochemical dynamics of fermentation in yeast Saccharomyces Cerevisiae
The zymase activity of the yeast Saccharomyces Cerevisiae is sensitive to environmental parameters and is therefore used as a microbiological sensor for water quality assessment, ecotoxicological characterization or envi…
A Deep Neural Network -- Mechanistic Hybrid Model to Predict Pharmacokinetics in Rat
An important aspect in the development of small molecules as drugs or agro-chemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical…