ArticleThe Journal of nutrition2021
Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.
Article in The Journal of nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.Food science & nutrition · 2026Article
- Cubes and playdough are feasible and acceptable methods to estimate food group intake with the Global Diet Quality Score app.Frontiers in nutrition · 2026Article
- Developing a Simple Non-Laboratory-Based Machine Learning Tool for Prediabetes Screening in a Target Population: A Proof-of-Concept Study.Journal of diabetes science and technology · 2025Article
- The association of global and prime diet quality scores with sperm quality parameters: a cross-sectional study.Journal of health, population, and nutrition · 2025Article
- Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.BMC public health · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- The global diet quality score as an indicator of adequate nutrient intake and dietary quality - a nation-wide representative study.Nutrition journal · 2024Article
- Performance of the Global Diet Quality Score (GDQS) App in Predicting Nutrient Adequacy and Metabolic Risk Factors among Thai Adults.The Journal of nutrition · 2023Article
- Article
- Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.Journal of diabetes investigation · 2023Article
- Article
- Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.Computational and mathematical methods in medicine · 2022Article
- Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis.Indian journal of endocrinology and metabolismReview
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Authors and funding
13 authors at 7 institutions in 4 countries.
Funding
Abstract
backgroundThe prevalence of type 2 diabetes has increased substantially in India over the past 3 decades. Undiagnosed diabetes presents a public health challenge, especially in rural areas, where access to laboratory testing for diagnosis may not be readily available.
objectivesThe present work explores the use of several machine learning and statistical methods in the development of a predictive tool to screen for prediabetes using survey data from an FFQ to compute the Global Diet Quality Score (GDQS).
methodsThe outcome variable prediabetes status (yes/no) used throughout this study was determined based upon a fasting blood glucose measurement ≥100 mg/dL. The algorithms utilized included the generalized linear model (GLM), random forest, least absolute shrinkage and selection operator (LASSO), elastic net (EN), and generalized linear mixed model (GLMM) with family unit as a (cluster) random (intercept) effect to account for intrafamily correlation. Model performance was assessed on held-out test data, and comparisons made with respect to area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.
resultsThe GLMM, GLM, LASSO, and random forest modeling techniques each performed quite well (AUCs >0.70) and included the GDQS food groups and age, among other predictors. The fully adjusted GLMM, which included a random intercept for family unit, achieved slightly superior results (AUC of 0.72) in classifying the prediabetes outcome in these cluster-correlated data.
conclusionsThe models presented in the current work show promise in identifying individuals at risk of developing diabetes, although further studies are necessary to assess other potentially impactful predictors, as well as the consistency and generalizability of model performance. In addition, future studies to examine the utility of the GDQS in screening for other noncommunicable diseases are recommended.
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