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Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes

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Machine Learning Models for Prediction of Diabetic Microvascular Complications

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Evaluation of the Low Carb Program Digital Intervention for the Self-Management of Type 2 Diabetes and Prediabetes in an NHS England General Practice: Single-Arm Prospective Study

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Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

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Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population

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Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre‐diabetes and diabetes

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Prediction of Diabetes Mellitus Progression Using Supervised Machine Learning

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Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices

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Risk prediction of diabetes and pre-diabetes based on physical examination data

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Nomogram model and risk score to predict 5‐year risk of progression from prediabetes to diabetes in Chinese adults: Development and validation of a novel model

Han, Y. Y., Hu, H., Liu, Y., Wang, Z., & Liu, D. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14910 · Cited by 17 · see citing works

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

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Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes

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Detection of diabetes from whole-body MRI using deep learning

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Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes

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Detection of Lower Albuminuria Levels and Early Development of Diabetic Kidney Disease Using an Artificial Intelligence-Based Rule Extraction Approach

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Predicting changes in glycemic control among adults with prediabetes from activity patterns collected by wearable devices

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Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus

Parkhi, D., Periyathambi, N., Ghebremichael‐Weldeselassie, Y., Patel, V. B., Sukumar, N., Siddharthan, R., Narlikar, L., & Saravanan, P. (2023) · iScience · doi:10.1016/j.isci.2023.107846 · Cited by 14 · see citing works

Weight Loss in a Digital Diabetes Prevention Program for People in Health Professional Shortage and Rural Areas

Graham, S. A., Auster‐Gussman, L. A., Lockwood, K. G., & Branch, O. H. (2023) · Population Health Management · doi:10.1089/pop.2022.0278 · Cited by 13 · see citing works

Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm

Schwartz, J. L., Tseng, E., Maruthur, N. M., & Rouhizadeh, M. (2021) · JMIR Medical Informatics · doi:10.2196/29803 · Cited by 13 · see citing works

Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

Zhang, B., Zhang, X., Luo, Z., Ren, J., Yu, X., Zhao, H., Wang, Y., Zhang, W., Wei-wei, T., Wei, X., et al. (2023) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2023.08.005 · Cited by 13 · see citing works

Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy

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Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

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Reach of a Fully Digital Diabetes Prevention Program in Health Professional Shortage Areas

Auster‐Gussman, L. A., Lockwood, K. G., Graham, S. A., Stein, N., & Branch, O. H. (2022) · Population Health Management · doi:10.1089/pop.2021.0283 · Cited by 12 · see citing works

Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program

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An Electronic Health Record–Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data

Kent, D. M., Nelson, J., Pittas, A. G., Colangelo, F. R., Koenig, C., Klaveren, D. V., Ciemins, E. L., & Cuddeback, J. K. (2021) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2021.09.012 · Cited by 12 · see citing works

Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort

Joglekar, M. V., Kunte, P. S., Wong, W. K. M., Bhat, D., Satoor, S. N., Patil, R. R., Karandikar, M. S., Fall, C. H. D., Yajnik, C. S., & Hardikar, A. A. (2022) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s2040174422000137 · Cited by 12 · see citing works

Breath Analysis Using Quartz Tuning Forks for Predicting Blood Glucose Levels Using Artificial Neural Networks

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An interpretable predictive deep learning platform for pediatric metabolic diseases

Javidi, H., Mariam, A., Alkhaled, L., Pantalone, K. M., & Rotroff, D. M. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae049 · Cited by 12 · see citing works

Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia

Zhang, X., Yao, W., Wang, D., Hu, W., Zhang, G., & Zhang, Y. (2024) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70003 · Cited by 11 · see citing works

An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data

Oh, R., Lee, H. K., Pak, Y. K., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19105800 · Cited by 10 · see citing works

Longitudinal artificial intelligence-based deep learning models for diagnosis and prediction of the future occurrence of polyneuropathy in diabetes and prediabetes

Lai, Y., Chiu, W.-C., Huang, C., et al. (2024) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2024.102982 · Cited by 10 · see citing works

Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model

Aoki, J., Khalid, O. M., Kaya, C., Nagymányoki, Z., Hussong, J. W., & Salama, M. E. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0052 · Cited by 10 · see citing works

Development and Validation of a Diabetes Risk Prediction Model With Individualized Preventive Intervention Effects

Jaeger, B. C., Casanova, R., Demesie, Y. N., Stafford, J. M., Wells, B. J., & Bancks, M. P. (2025) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaf250 · Cited by 9 · see citing works

Screening for Prediabetes and Diabetes in a National Network of Federally Qualified Health Centers: An Observational Study

O’Brien, M. J., Bailey, S. C., Gregory, D. L., Owen, A. L., Khan, S. S., Ackermann, R. T., Hassan, A., Mohanty, N., & Bowen, M. E. (2023) · Journal of general internal medicine · doi:10.1007/s11606-023-08402-1 · Cited by 9 · see citing works

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics

Sahid, M. A., Babar, M. U. H., & Uddin, M. P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300785 · Cited by 9 · see citing works

Screening for pre-diabetes using support vector machine model

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Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

Morgan-Benita, J. A., Sánchez-Reyna, A. G., Espino-Salinas, C. H., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12112803 · Cited by 9 · see citing works

Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators

Chen, X., Zhou, S., Yang, L., Zhong, Q.-Q., Liu, H.-G., Zhang, Y.-J., Yu, H., & Cai, Y. (2024) · Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy · doi:10.2147/dmso.s449955 · Cited by 9 · see citing works

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus

Li, Z., Li, Y., Mao, Z., Wang, C., Hou, J., Zhao, J., Wang, J., Tian, Y., & Li, L. (2025) · Nutrients · doi:10.3390/nu17060947 · Cited by 9 · see citing works

Screening for diabetes and impaired glucose metabolism in Qatar: Models’ development and validation

Sadek, K., Abdelhafez, I., Al‐Hashimi, I., et al. (2021) · Primary care diabetes · doi:10.1016/j.pcd.2021.10.002 · Cited by 8 · see citing works

The Development of a Text Messaging Platform to Enhance a Youth Diabetes Prevention Program: Observational Process Study

Sapre, M., Elaiho, C. R., Prayaga, R. B., Prayaga, R. S., Constable, J., & Vangeepuram, N. (2024) · JMIR Formative Research · doi:10.2196/45561 · Cited by 8 · see citing works

Postprandial Metabolite Profiles and Risk of Prediabetes in Young People: A Longitudinal Multicohort Study

Goodrich, J. A., Wang, H., Walker, D. I., Lin, X., Hu, X. Y., Alderete, T. L., Chen, Z., Valvi, D., Baumert, B. O., Rock, S., et al. (2023) · Diabetes Care · doi:10.2337/dc23-0327 · Cited by 8 · see citing works

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up

Liu, C.-H., Chang, C.-F., Chen, I.-C., Lin, F.-M., Tzou, S., Hsieh, C.-B., Chu, T.-W. D., & Pei, D. (2024) · Diagnostics · doi:10.3390/diagnostics14100979 · Cited by 8 · see citing works

Consumer-oriented review of digital diabetes prevention programs: insights from the CDC’s diabetes prevention recognition program

Lalani, B., Shim, J., Vadini, V., Valdez, Y., Zade, D., & Mathioudakis, N. (2025) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2025.1562108 · Cited by 7 · see citing works

Retention and outcomes of National Diabetes Prevention Program enrollees and non-enrollees with prediabetes: The University of Michigan experience

Herman, W. H., Santos, C. V., Joiner, K. L., & McEwen, L. N. (2023) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2023.108527 · Cited by 7 · see citing works

Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine

Byeon, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1013162 · Cited by 7 · see citing works

After-meal blood glucose level prediction for type-2 diabetic patients

Ahmed, B. M., Ali, M. E., Masud, M. M., Azad, M. R., & Naznin, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28855 · Cited by 6 · see citing works

Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus

Zhu, L., Huang, Q., Li, X., Jin, B., Ding, Y., Chou, C. J., Su, K.-J., Zhang, Y., Chen, X., Hwa, K., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.841209 · Cited by 6 · see citing works

Regulatory Framework for Clinical Decision Support Software: Present Uncertainty and Prospective Proposition

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Predicting Prediabetes Using Simple a Multi-Layer Perceptron Neural Network Model

Vîrgolici, O., & Vîrgolici, H. (2023) · Studies in health technology and informatics · doi:10.3233/shti230453 · Cited by 6 · see citing works

Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes

Frank, S., Hames, K. C., Jbaily, A., Park, J. H., Stroyeck, C., & Price, D. A. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0189 · Cited by 5 · see citing works

The Low Carb Program for people with type 2 diabetes and pre-diabetes: a mixed methods feasibility study of signposting from general practice

Scott, E. J., Shehata, M., Panesar, A., Summers, C., & Dale, J. R. (2021) · BJGP Open · doi:10.3399/bjgpo.2021.0137 · Cited by 4 · see citing works

Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features

Jain, N., Patel, B. B., Hanawal, M. K., Lila, A. R., Memon, S. S., Bandgar, T., & Kumar, A. (2023) · Metabolomics · doi:10.1007/s11306-023-02066-y · Cited by 4 · see citing works

The impact of ethnicity and intra-pancreatic fat on the postprandial metabolome response to whey protein in overweight Asian Chinese and European Caucasian women with prediabetes

Joblin-Mills, A., Wu, Z. E., Fraser, K., Jones, B., Yip, W. C. Y., Lim, J. J., Lu, L. W., Sequeira, I. R., & Poppitt, S. D. (2022) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2022.980856 · Cited by 4 · see citing works

Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm

Cui, Q., Pu, J., Li, W., et al. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1368225 · Cited by 4 · see citing works

Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States – A machine learning approach

Okere, A. N., Li, T. Y., Theran, C. A., Nyasani, E., & Ali, A. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109824 · Cited by 4 · see citing works

Differential risk assessment in persons at risk of type 2 diabetes using urinary peptidomics

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Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Lukić, I., Savić, N., Simic, M., Ranković, N., Ranković, D., & Lazić, L. (2021) · Medicina · doi:10.3390/medicina58010009 · Cited by 3 · see citing works

Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records

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Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study

Xu, L., Sun, X., Wang, N., Wang, Y., Yan, L., & Zhang, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-88073-6 · Cited by 3 · see citing works

Development and validation of risk prediction models for new-onset type 2 diabetes in adults with impaired fasting glucose

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Aberrations in the early pregnancy serum metabolic profile in women with prediabetes at two years postpartum

Muhli, E., Benchraka, C., Lotankar, M., Houttu, N., Niinikoski, H., Lahti, L., & Laitinen, K. (2023) · Metabolomics · doi:10.1007/s11306-023-01994-z · Cited by 2 · see citing works

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach

Michel, L. J., Rospleszcz, S., Reisert, M., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000429 · Cited by 2 · see citing works

Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study

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A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor

Karim, F., Anderson, J. H., Currie, K., BUI, C., Klyve, D., & Somers, V. K. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0170 · Cited by 2 · see citing works

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach

Foppiani, A., Amicis, R. S. D., Leone, A., Sileo, F., Mambrini, S. P., Menichetti, F., Pozzi, G., Bertoli, S., & Battezzati, A. (2024) · European Journal of Clinical Nutrition · doi:10.1038/s41430-024-01495-9 · Cited by 1 · see citing works

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques

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Construction and Validation of a Model for Predicting Impaired Fasting Glucose Based on More Than 4000 General Population

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A formula based on autonomic test using EZSCAN and anthropometric data for diagnosis of DM in China

Zhao, X., Getmanenko, A., Zhang, Y., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61841-2

GLSTM: On Using LSTM for Glucose Level Prediction

Kashif, M. J., Flesca, S., & Veltri, P. (2024) · Studies in health technology and informatics · doi:10.3233/shti240067

An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial

Mathioudakis, N., Lalani, B., Abusamaan, M. S., Alderfer, M., Alver, D., Dobs, A., Kane, B., McGready, J., Riekert, K., Ringham, B., et al. (2025) · JAMA · doi:10.1001/jama.2025.19563 · Cited by 41 · see citing works

Prevalence of diabetes related vascular complications in subjects with normal glucose tolerance, prediabetes, newly detected diabetes and known diabetes

Yadav, R., Jain, N., Raizada, N., Jhamb, R., Rohatgi, J., & Madhu, S. V. (2021) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2021.102226 · Cited by 12 · see citing works

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

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Breakdown of Machine Learning Algorithms

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Interpretable Machine Learning Assessment

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Machine Learning Techniques for the Management of Diseases: A Paper Review

Tim, N. K., Kamla, V. C., & Tagne, E. F. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-56396-6_23