Development of various diabetes prediction models using machine learning techniques
Open accessAbstract
Background: There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.Methods: Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.Results: The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.Conclusion: We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.
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Machine learning and artificial intelligence approaches have demonstrated growing promise in diabetes risk prediction, complication detection and healthcare utilization forecasting.
“More recently, machine learning and artificial intelligence approaches have demonstrated growing promise in diabetes risk prediction, complication detection and healthcare utilization forecasting [21–28].”
From Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health (2026) · cites this paper as [21]
The Discussion reports good performance for the paper’s 1- and 2-year diabetes prediction models, supporting the risk-prediction part of the claim. The paper does not report machine-learning detection of diabetes complications or forecasting of healthcare utilization. Our copy provides section labels but no page numbers. (LLM, conf=0.88)
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Cites
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011 · cited 1× · see citing works
“Once the risk of DM occurrence is recognized, early intervention, including healthy dietary habits and regular physical activity, helps slow down the progression from a prediabetic to a diabetic condition [5-7].”
BackgroundTuomilehto, J., Lindström, J., Eriksson, J. G., Valle, T. T., Hämäläinen, H., Ilanne-Parikka, P., Keinänen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200105033441801 · Cited by 10,873 · cited 1× · see citing works
“Once the risk of DM occurrence is recognized, early intervention, including healthy dietary habits and regular physical activity, helps slow down the progression from a prediabetic to a diabetic condition [5-7].”
BackgroundPan, X.-R., Li, G.-W., Hu, Y.-H., Wang, J.-X., Yang, W.-Y., An, Z.-X., Hu, Z.-X., Juan-Lin, Xiao, J.-Z., Cao, H.-B., et al. (1997) · Diabetes Care · doi:10.2337/diacare.20.4.537 · Cited by 4,320 · see citing works
Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., & Khunti, K. (2007) · BMJ · doi:10.1136/bmj.39063.689375.55 · Cited by 1,171 · cited 1× · see citing works
“gestational diabetes, and insulin resistance, emphasizing the need for early diagnosis and treatment [2-4].”
BackgroundData Mining in Healthcare and Biomedicine: A Survey of the Literature
Yoo, I., Alafaireet, P. E., Marinov, M. D., Pena-Hernandez, K. E., Gopidi, R., Chang, J.-F., & Hua, L. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9710-5 · Cited by 547 · see citing works
Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.01.004 · Cited by 499 · see citing works
Machine Learning for Detection and Diagnosis of Disease
Sajda, P. (2006) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.8.061505.095802 · Cited by 414 · see citing works
Diabetes Fact Sheet in Korea, 2016: An Appraisal of Current Status
Won, J. C., Lee, J. H., Kim, J. H., Kang, E. S., Won, K. C., Kim, D. J., & Lee, M. (2018) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2018.0017 · Cited by 301 · see citing works
Self-citationLindström, J., Peltonen, M., Eriksson, J. G., Louheranta, A., Fogelholm, M., Uusitupa, M., & Tuomilehto, J. (2006) · Diabetologia · doi:10.1007/s00125-006-0198-3 · Cited by 293 · cited 1× · see citing works
“gestational diabetes, and insulin resistance, emphasizing the need for early diagnosis and treatment [2-4].”
BackgroundA Risk Score for Predicting Incident Diabetes in the Thai Population
Aekplakorn, W., Bunnag, P., Woodward, M., Sritara, P., Cheepudomwit, S., Yamwong, S., Yipintsoi, T., & Rajatanavin, R. (2006) · Diabetes Care · doi:10.2337/dc05-2141 · Cited by 275 · cited 1× · see citing works
“Other studies also showed similar degrees of improvement when fasting glucose and HbA1c levels were included [22-27].”
Result2019 Clinical Practice Guidelines for Type 2 Diabetes Mellitus in Korea
Kim, M. K., Ko, S., Kim, B., Kang, E. S., Noh, J., Kim, S., Park, S.-O., Hur, K. Y., Chon, S., Moon, M. K., et al. (2019) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2019.0137 · Cited by 213 · see citing works
Self-citationSchulze, M. B., Weikert, C., Pischon, T., Bergmann, M. M., Al‐Hasani, H., Schleicher, E. D., Fritsche, A., Häring, H., Boeing, H., & Joost, H. (2009) · Diabetes Care · doi:10.2337/dc09-0197 · Cited by 141 · see citing works
Diabetes Fact Sheets in Korea, 2018: An Appraisal of Current Status
Kim, B., Won, J. C., Lee, J. H., Kim, H., Park, J. H., Ha, K. H., Won, K. C., Kim, D. J., & Park, K. S. (2019) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2019.0067 · Cited by 129 · see citing works
Self-citationScreening for Prediabetes Using Machine Learning Models
Choi, S. B., Kim, W. J., Yoo, T. K., Park, J. S., Chung, J. W., Lee, Y., Kang, E. S., & Kim, D. W. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/618976 · Cited by 108 · see citing works
Silva, K. D., Lee, W. K., Forbes, A. B., Demmer, R. T., Barton, C., & Enticott, J. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104268 · Cited by 108 · see citing works
Wang, C., Li, L., Wang, L., Ping, Z., Flory, M. T., Wang, G., Xi, Y., & Li, W. (2013) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.01.023 · Cited by 94 · see citing works
Computational Intelligence in Early Diabetes Diagnosis: A Review
Shankaracharya, N. A., Odedra, D., Samanta, S., & Vidyarthi, A. S. (2010) · The Review of Diabetic Studies · doi:10.1900/rds.2010.7.252 · Cited by 93 · see citing works
Proceed with Caution When Using Real World Data and Real World Evidence
Kim, H., & Kim, J. H. (2019) · Journal of Korean Medical Science · doi:10.3346/jkms.2019.34.e28 · Cited by 82 · cited 1× · see citing works
“One of the study strengths was data management [28,29].”
BackgroundSelf-citationAlssema, M., Vistisen, D., Heymans, M., Nijpels, G., Glümer, C., Zimmet, P., Shaw, J., Eliasson, M., Stehouwer, C. D., Tabák, Á., et al. (2010) · Diabetologia · doi:10.1007/s00125-010-1990-7 · Cited by 77 · cited 1× · see citing works
“766 despite using a larger sample size of 18,301 subjects [18].”
BackgroundNanri, A., Nakagawa, T., Kuwahara, K., Yamamoto, S., Honda, T., Okazaki, H., Uehara, A., Yamamoto, M., Miyamoto, T., Kochi, T., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142779 · Cited by 72 · cited 1× · see citing works
“882 by adding fasting glucose and HbA1c levels, respectively [21].”
BackgroundMedical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration
Kim, H., Kim, D., & Yoon, K. (2019) · Endocrinology and Metabolism · doi:10.3803/enm.2019.34.4.349 · Cited by 68 · cited 1× · see citing works
“One of the study strengths was data management [28,29].”
BackgroundSelf-citationSun, F., Tao, Q., & Zhan, S. (2009) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2009.05.005 · Cited by 67 · cited 1× · see citing works
“Other studies also showed similar degrees of improvement when fasting glucose and HbA1c levels were included [22-27].”
ResultTwo risk score models for predicting incident Type 2 diabetes in Japan
Doi, Y., Ninomiya, T., Hata, J., Hirakawa, Y., Mukai, N., Iwase, M., & Kiyohara, Y. (2011) · Diabetic Medicine · doi:10.1111/j.1464-5491.2011.03376.x · Cited by 60 · cited 1× · see citing works
“Other studies also showed similar degrees of improvement when fasting glucose and HbA1c levels were included [22-27].”
ResultPrediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning
Casanova, R., Saldana, S. J., Simpson, S. L., LACY, M. E., Subauste, A. R., Blackshear, C. T., Wagenknecht, L. E., & Bertoni, A. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163942 · Cited by 53 · cited 2× · see citing works
“820 after analyzing 93 parameters of 3,363 individuals; however, they made an eight-year prediction [20].”
BackgroundMethodHeianza, Y., Arase, Y., Hsieh, S. D., Saito, K., Tsuji, H., Kodama, S., Tanaka, S., Ohashi, Y., Shimano, H., Yamada, N., et al. (2012) · Diabetologia · doi:10.1007/s00125-012-2712-0 · Cited by 52 · see citing works
Risk assessment tools for detecting those with pre-diabetes: A systematic review
Barber, S. R., Davies, M. J., Khunti, K. K., & Gray, L. J. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.03.007 · Cited by 49 · see citing works
National Health Screening Program of Korea
Lee, W.-C., & Lee, S. Y. (2010) · Journal of Korean Medical Association · doi:10.5124/jkma.2010.53.5.363 · Cited by 48 · cited 9× · see citing works
“Copyright © 2022 Korean Diabetes Association https://e-dmj.org D I A B E T E S & M E T A B O L I S M J O U R N A L Development of Various Diabetes Prediction Models Using Machine Learning Techniques Juyoung Shin1,2, Jaewon Kim3, Chanjung Lee3, Joon Young Yoon3, Seyeon Kim3, Seungjae Song3, Hun-Sung Kim2,4 1Health Prom…”
BackgroundInfluentialGuasch‐Ferré, M., Bulló, M., Costa, B., Martínez‐González, M. Á., Ibarrola-Jurado, N., Estruch, R., Barrio, F., Salas‐Salvadó, J., & for the PREDI-PLAN Investigators (2012) · PLoS ONE · doi:10.1371/journal.pone.0033437 · Cited by 39 · cited 1× · see citing works
“Other studies also showed similar degrees of improvement when fasting glucose and HbA1c levels were included [22-27].”
ResultMashayekhi, M., Prescod, F., Shah, B., Dong, L., Keshavjee, K., & Guergachi, A. (2015) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2014.10.006 · Cited by 31 · see citing works
Cumulative exposure to impaired fasting glucose and future risk of type 2 diabetes mellitus
Kim, M. K., Han, K., Koh, E. S., Hong, O., Baek, K., Song, K., & Kwon, H. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108799 · Cited by 27 · cited 1× · see citing works
“[33] suggested that higher fasting glucose levels, even within the normal range, lead to a risk of diabetes.”
BackgroundRhee, S. Y., Sung, J. M., Kim, S., Cho, I., Lee, S., & Chang, H. (2021) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0081 · Cited by 20 · cited 1× · see citing works
“[32] developed a diabetes prediction model using the NHIS cohort of Korea, which outperformed the conventional model.”
MethodSelf-citationKim, T. M., Kim, H., Jeong, Y. J., Baik, S. J., Yang, S. J., Lee, S., Cho, J., Lee, H., Yim, H. W., Choi, I. Y., et al. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4237 · Cited by 15 · cited 1× · see citing works
“gestational diabetes, and insulin resistance, emphasizing the need for early diagnosis and treatment [2-4].”
BackgroundSelf-citationChoi, H. S., Lee, S. W., Kim, J. T., & Lee, H. K. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2019.0109 · Cited by 15 · cited 1× · see citing works
“It was suggested that some of them were independent risk factors for developing diabetes, where forced vital capacity and forced expiratory volume in one-second correlated significantly with the incidence of diabetes [19].”
BackgroundSelf-citationPrediction of Incident Diabetes Mellitus in Middle-aged Adults: The Framingham Offspring Study
Wilson, P. W. F. (2007) · Archives of Internal Medicine · doi:10.1001/archinte.167.10.1068 · Cited by 4 · cited 1× · see citing works
“For instance, the Framingham Diabetes Risk Scoring model [30], a wellknown diabetes predictor based on a middle-aged United States population with an AUC of 0.”
Flagged first, then by citations
Cited by
Sorted by citations
Moghaddam, M. T., Jahani, Y., Arefzadeh, Z., Dehghan, A., Khaleghi, M., Sharafi, M., & Nikfar, G. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02341-z · Cited by 25 · see citing works
“Popular ML models used in diabetes prediction include Random Forest, K-NN, neural networks, support vector machines, decision trees, and extra trees [7, 9].”
MethodChun, J., & Kim, H. (2023) · Journal of Korean Medical Science · doi:10.3346/jkms.2023.38.e253 · Cited by 21 · see citing works
Self-citationImproving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
Shin, J., Lee, J., Ko, T., Lee, K. H., Choi, Y., & Kim, H. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12111899 · Cited by 19 · see citing works
“, in which feature importance proved the effect of 27 selected variables to fit the simplified model [15].”
BackgroundMethodResultInfluentialSelf-citationTanaka, M., Akiyama, Y., Mori, K., Hosaka, I., Kato, K., Endo, K., Ogawa, T., Sato, T., Suzuki, T., Yano, T., et al. (2023) · Diabetes Epidemiology and Management · doi:10.1016/j.deman.2023.100191 · Cited by 17 · see citing works
Applications of artificial intelligence and machine learning in prediabetes: a scoping review.
Lalani, B., Herur, R., Zade, D., Collins, G., Dishong, D. M., Mehta, S., Shim, J., Valdez, Y., & Mathioudakis, N. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251351995 · Cited by 11 · see citing works
Federated multimodal AI for precision-equitable diabetes care
Bai, B., Liu, X., & Li, H. Q. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1678047 · Cited by 10 · see citing works
“The robustness of gradient boosting machines (GBM) and random forest (RF) algorithms in diabetes risk prediction has been demonstrated by their high AUC values (198, 199).”
Artificial intelligence applied to diabetes complications: a bibliometric analysis
Tao, Y.-H., Hou, J., Zhou, G., & Zhang, D. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1455341 · Cited by 10 · see citing works
Kim, H., Jung, D. Y., Lee, S., Cho, J., Yim, H. W., & Kim, H. (2022) · Journal of Diabetes · doi:10.1111/1753-0407.13315 · Cited by 7 · see citing works
“However, because this study used data from medical examinations at university hospitals, it contained many variables.(37) Lastly, the medical check-up did not include postprandial blood glucose measurement, one of the main diagnostic criteria for diabetes.”
MethodSelf-citationA Survey on Diabetes Mellitus Using Machine Learning Classifiers
Sinha, N. K., Pundir, M., & Goel, P. (2025) · International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) · doi:10.1109/cictn64563.2025.10932388 · Cited by 5 · see citing works
Liu, Y.-M., Chang, T.-W., Lee, L.-C., Chen, C., Hsu, P.-S., Tsan, Y., Yang, C., & Chu, W. (2024) · Diagnostics · doi:10.3390/diagnostics15010072 · Cited by 5 · see citing works
Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data
Lee, H. A., Park, H., & Hong, Y. S. (2024) · Journal of Korean Medical Science · doi:10.3346/jkms.2024.39.e47 · Cited by 4 · see citing works
“25 34,35 Although the Framingham offspring study developed a simple predictive , the use of a parental history of diabetes is challenging.”
BackgroundMethodResultJain, 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
“Few studies have utilized machine learning (ML) algorithms to predict diabetes using externally observed parameters instead of molecular information (Fan et al., 2021; Mujumdar and Vaidehi, 2019; Shin et al., 2022).”
BackgroundSinap, V. (2025) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1556260 · Cited by 3 · see citing works
Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques
Konda, R., Ramineni, A., Jayashree, J., Singavajhala, N., & Vanka, S. A. (2024) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.10.5497 · Cited by 2 · see citing works
“A systematic evaluation of artificial intelligence and machine learning-based diabetic mellitus detection and self-management was carried out by Chaki et al. in 2022 [12].”
Background(2024) · Medicine & health · doi:10.17576/mh.2024.1902.03 · Cited by 1 · see citing works
An Intelligent Supervised Machine Learning Framework for Diabetes Prediction
Maenge, A., Sigwele, T., Kuthadi, V. M., & Selvaraj, R. (2024) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig64922.2024.10911609 · Cited by 1 · see citing works
Soni, A., Shah, A., Modi, K., & Doshi, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06668-8_41 · Cited by 1 · see citing works
An Explainable Transformer‐Based Model for Predicting Chronic Diseases Risk
Xu, N., Zhang, K., & Tian, J. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70739 · Cited by 1 · see citing works
“For diabetes, Ha et al. developed a 10-year risk score for Korean adults [22], and Shin et al. identified fasting blood glucose and HbA1c as critical indicators [23].”
Lee, S.-Y., Saleem, M., Land, A. M., DeLaney, E. W., Garretson, A. R., Patel, M., Hall, A. G., Westrick, S., Ovalle, F., Cherrington, A. L., et al. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106604
Ye, B., Shen, Z., Ji, A., Jiao, Y., Niu, Y., Zuo, K., Hu, P., & Li, W. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice63571.2024.10864273
Lee, S.-Y., Saleem, M., Land, A. M., Delaney, E. W., Garretson, A., Patel, M., Hall, A. G., Westrick, S. C., Ovalle, F., Cherrington, A., et al. (2025) · doi:10.2196/preprints.72624
Efficient Diabetes Detection using Hybrid Machine Learning Model
Gowri, A. S., Jose, P., Karthik, S. M., Shaheema, S. B., Karuppasway, K. M., & Balamurugan, R. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939825
“Shin, Juyoung [19] proposed classifier based on decision tree provides a means of targeting early diabetes prevention of the healthcare.”
A Case Study Of Machine Learning Methodologies For The Diabetic Prediction
Kumar, M., & Geetha, D. K. (2025) · International Journal of Environmental Sciences · doi:10.64252/3smhb622
Diabetes Prediction Using Machine Learning
Paikrao, P. D., Thakare, R., Deogaonkar, A., Hudge, S., & Jaiswal, S. (2025) · Global Conference in Emerging Technology (GINOTECH) · doi:10.1109/ginotech63460.2025.11076635
Type 2 Diabetes Mellitus Early Detection with Machine Learning-Based Prediction Modalities
Afzal, A. H., Alam, O., Zafar, S., & Aalam, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5723-0_1
Designing Intelligent and Integrated Analytics Frameworks for Personalized Healthcare
Sudharson, D., N, P. K., Rakshitha, S., Sri Dharaneesh, P., Diwakaran, M., & Saravanan, A. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330909
“[16] Noted dashboards improve clinician efficiency and satisfaction.”
Ghatrora, K. K., Verma, A., Bhatia, T. K., Deepali, & Singh, Y. (2025) · International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) · doi:10.1109/iot-siu65919.2025.11402868
Mahabhoi, A., Bhuyan, S., Lai, D. T. C., & Dash, S. R. (2025) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit66168.2025.11400198
“Approaches using classical ML models such as Logistic Regression (LR), Support Vector Machines (SVM), Decision Trees (DT) and k-Nearest Neighbours (k-NN) have been extensively used for diabetes prediction [1], [8].”
Daw, S., Mondal, B., Chakraborty, D., Paul, S., Halder, I., & Maji, B. (2026) · International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) · doi:10.1109/etaact69135.2026.11541351
Metaheuristic Hyperparameter Optimization of Machine Learning Models for Diabetes Prediction
Dayanan, H., Akdağlı, A., & Lale, T. (2026) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.2015092
- Authors
- Shin, J., Kim, J., Lee, C., Yoon, J. Y., Kim, S., Song, S., & Kim, H.-S.
- Affiliations
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Catholic University of Korea (KR)
- Corresponding author
- Kim, Hun-Sung
- Venue
- Diabetes & Metabolism Journal
- Publisher
- Korean Diabetes Association
- Published
- 2022-07-31
- Volume
- 46
- Issue
- 4
- Pages
- 650-657
- ISSN
- 2233-6079 (ISSN-L)
- 2233-6087
- PMC
- PMC9353566
- OpenAlex
- W4221120492
- Cited by
- 29
- References
- 34
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 8 · 2023: 3 · 2022: 2
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diabetes & Metabolism Journal (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- diabetes prediction, diabetes risk prediction models, fasting blood glucose, gradient boosting, random forest, health screening
- MeSH terms
- Machine Learning, Algorithms, Diabetes Mellitus, Humans, Prospective Studies, ROC Curve
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