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Cited by 261Artificial Intelligence in HealthcareMachine Learning in HealthcareData Mining and Machine Learning Applications

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

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Deberneh, Henock M., Kim, Intaek·2021·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph18063317PMC PMC8004981
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Abstract

Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.

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  • AI algorithms can predict the onset of type 2 diabetes and its complications with remarkable accuracy.

    “Various studies have shown that AI algorithms can predict the onset of T2D and its complications with remarkable accuracy (13-15).”

    From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [13]

    The Results (§4.2) report that the model predicted T2D occurrence in the following year, with a best test-set accuracy of 73%. The paper does not report predicting complications; its Conclusions (§6) mention them only as outcomes patients might mitigate using the prediction. It describes performance as “reasonably good,” not remarkable. (LLM, conf=0.89)

    Experimental — accuracy not yet measured

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  1. [2]Shaw J. Sicree R. Zimmet P. Global estimates of the prevalence of diabetes for 2010 and 2030 Diabetes Res. Clin. Pract. 2010 87 4 14 10.1016/j.diabres.2009.10.007 19896746 · DOI · Paper page
  2. [3]Zou Q. Qu K. Luo Y. Yin D. Ju Y. Tang H. Predicting diabetes mellitus with machine learning techniques Front. Genet. 2018 9 515 10.3389/fgene.2018.00515 30459809 PMC6232260 · DOI
  3. [4]Won J.C. Lee J.H. Kim J.H. Kang E.S. Won K.C. Kim D.J. Lee M.-K. Diabetes fact sheet in Korea, 2016: An appraisal of current status Diabetes Metab. J. 2018 42 415 424 10.4093/dmj.2018.0017 30113146 PMC6202557 · DOI
  4. [5]Choi S.B. Kim W.J. Yoo T.K. Park J.S. Chung J.W. Lee Y.-H. Kang E.S. Kim D.W. Screening for prediabetes using machine learning models Comput. Math. Methods Med. 2014 2014 1 8 10.1155/2014/618976 25165484 PMC4140121 · DOI
  5. [6]Deberneh H.M. Kim I. Park J.H. Cha E. Joung K.H. Lee J.S. Lim D.S. 1233-P: Prediction of type 2 diabetes occurrence using machine learning model Am. Diabetes Assoc. 2020 69 1233 10.2337/db20-1233-P · DOI
  6. [7]Buch V. Varughese G. Maruthappu M. Artificial intelligence in diabetes care Diabet. Med. 2018 35 495 497 10.1111/dme.13587 29368355 · DOI
  7. [9]Woldaregay A.Z. Årsand E. Botsis T. Albers D. Mamykina L. Hartvigsen G. Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in type 1 diabetes J. Med. Internet Res. 2019 21 e11030 10.2196/11030 31042157 PMC6658321 · DOI
  8. [10]Maniruzzaman Kumar N. Abedin M. Islam S. Suri H.S. El-Baz A.S. Suri J.S. Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm Comput. Methods Programs Biomed. 2017 152 23 34 10.1016/j.cmpb.2017.09.004 29054258 · DOI · Paper page
  9. [11]Kavakiotis I. Tsave O. Salifoglou A. Maglaveras N. Vlahavas I. Chouvarda I. Machine learning and data mining methods in diabetes research Comput. Struct. Biotechnol. J. 2017 15 104 116 10.1016/j.csbj.2016.12.005 28138367 PMC5257026 · DOI · Paper page
  10. [12]Ravaut M. Sadeghi H. Leung K.K. Volkovs M. Rosella L.C. Diabetes mellitus forecasting using population health data in Ontario, Canada Proc. Mach. Learn. Res. 2019 85 1 18
  11. [13]Böhning D. Multinomial logistic regression algorithm Ann. Inst. Stat. Math. 1992 44 197 200 10.1007/BF00048682 · DOI
  12. [14]Breiman L. Random forests Mach. Learn. 2001 45 5 32 10.1023/A:1010933404324 · DOI · Paper page
  13. [15]Cortes C. Vapnik V. Support-vector networks Mach. Learn. 1995 20 273 297 10.1007/BF00994018 · DOI · Paper page
  14. [17]Park D.-C. Jeong T. Lee Y. Min S.-Y. Satellite Image Classification using a Classifier Integration Model Proceedings of the 2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) Sharm El-Sheikh, Egypt 27–30 June 2011 90 94
  15. [19]Aggarwa C.C. Data Classification: Algorithms and Applications Data Mining and Knowledge Discovery Series CRC Press Boca Raton, FL, USA 2014
  16. [20]Liu X. Faes L. Kale A.U. Wagner S.K. Fu D.J. Bruynseels A. Mahendiran T. Moraes G. Shamdas M. Kern C. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis Lancet Digit. Health 2019 1 e271 e297 10.1016/S2589-7500(19)30123-2 33323251 · DOI · Paper page
  17. [21]Choi B.G. Rha S.-W. Kim S.W. Kang J.H. Park J.Y. Noh Y.-K. Machine learning for the prediction of new-onset diabetes mellitus during 5-year follow-up in non-diabetic patients with cardiovascular risks Yonsei Med. J. 2019 60 191 199 10.3349/ymj.2019.60.2.191 30666841 PMC6342710 · DOI
  18. [23]Wei S. Zhao X. Miao C. A comprehensive exploration to the machine learning techniques for diabetes identification Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) Singapore 5–8 February 2018 291 295
  19. [24]Lovric M. Banic I. Lacic E. Kern R. Pavlovic K. Turkalj M. Predicting treatment outcomes using explainable machine learning in children with asthma Authorea Prepr. 2020 10.22541/au.159594002.27964108 PMC8151683 34068718 · DOI
  20. [26]Weston J. Mukherjee S. Chapelle O. Pontil M. Poggio T. Vapnik V. Feature selection for SVMs Advances in Neural Information Processing Systems 13 (NIPS 2000) MIT Press Cambridge, MA, USA 2001
  21. [27]Kira K. Rendell L.A. The Feature Selection Problem: Traditional Methods and a New Algorithm Association for the Advancement of Artificial Intelligence (AAAI) Menlo Park, CA, USA 1992 Volume 2 129 134
  22. [28]Jovic A. Brkic K. Bogunovic N. A Review of Feature Selection Methods with Applications Proceedings of the 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) Opatija, Croatia 25–29 May 2015 1200 1205
  23. [29]Ding H. Feng P.-M. Chen W. Lin H. Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis Mol. BioSyst. 2014 10 2229 2235 10.1039/C4MB00316K 24931825 · DOI
  24. [30]Bakar Z.A. Ispawi D.I. Ibrahim N.F. Tahir N.M. Classification of Parkinson’s Disease based on Multilayer Perceptrons (MLPs) Neural Network and ANOVA as a Feature Extraction Proceedings of the 2012 IEEE 8th International Colloquium on Signal Processing and its Applications Melaka, Malaysia 23–25 March 2015 63 67
  25. [31]Kim H.-Y. Analysis of variance (ANOVA) comparing means of more than two groups Restor. Dent. Endod. 2014 39 74 77 10.5395/rde.2014.39.1.74 24516834 PMC3916511 · DOI
  26. [33]You W. Yang Z. Ji G. Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination Expert Syst. Appl. 2014 41 1463 1475 10.1016/j.eswa.2013.08.043 · DOI
  27. [34]Granitto P.M. Furlanello C. Biasioli F. Gasperi F. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products Chemom. Intell. Lab. Syst. 2006 83 83 90 10.1016/j.chemolab.2006.01.007 · DOI
  28. [35]Yin Z. Zhang J. Operator functional state classification using least-square support vector machine based recursive feature elimination technique Comput. Methods Programs Biomed. 2014 113 101 115 10.1016/j.cmpb.2013.09.007 24138846 · DOI
  29. [36]García S. Luengo J. Herrera F. Data Preprocessing in Data Mining Springer Berlin/Heidelberg, Germany 2015 Volume 72
  30. [38]Rahman M.M. Davis D.N. Addressing the class imbalance problem in medical datasets Int. J. Mach. Learn. Comput. 2013 3 224 228 10.7763/IJMLC.2013.V3.307 · DOI · Paper page
  31. [39]Guo X. Yin Y. Dong C. Yang G. Zhou G. On the Class Imbalance Problem Proceedings of the 2008 Fourth International Conference on Natural Computation Jinan, China 18–20 October 2008 Volume 4 192 201
  32. [40]Chawla N.V. Bowyer K.W. Hall L.O. Kegelmeyer W.P. SMOTE: Synthetic minority over-sampling technique J. Artif. Intell. Res. 2002 16 321 357 10.1613/jair.953 · DOI · Paper page
  33. [41]Bunkhumpornpat C. Sinapiromsaran K. Lursinsap C. MUTE: Majority under-sampling technique Proceedings of the 2011 8th International Conference on Information, Communications & Signal Processing; Institute of Electrical and Electronics Engineers (IEEE) Singapore 13–16 December 2011 1 4
  34. [43]Inoue K. Matsumoto M. Kobayashi Y. The combination of fasting plasma glucose and glycosylated hemoglobin predicts type 2 diabetes in Japanese workers Diabetes Res. Clin. Pract. 2007 77 451 458 10.1016/j.diabres.2007.01.024 17346846 · DOI
  35. [44]Norberg M. Eriksson J.W. Lindahl B. Andersson C. Rolandsson O. Stenlund H. Weinehall L. A combination of HbA1c, fasting glucose and BMI is effective in screening for individuals at risk of future type 2 diabetes: OGTT is not needed J. Intern. Med. 2006 260 263 271 10.1111/j.1365-2796.2006.01689.x 16918824 · DOI
  36. [45]Čaušević A. Semiz S. Macić-Džanković A. Cico B. Dujić T. Malenica M. Bego T. Relevance of uric acid in progression of type 2 diabetes mellitus Bosn. J. Basic Med. Sci. 2010 10 54 59 10.17305/bjbms.2010.2736 20192932 PMC5596612 · DOI
  37. [46]Hutchinson M.S. Joakimsen R.M. Njølstad I. Schirmer H. Figenschau Y. Svartberg J. Jorde R. Effects of age and sex on estimated diabetes prevalence using different diagnostic criteria: The Tromsø OGTT Study Int. J. Endocrinol. 2013 2013 1 9 10.1155/2013/613475 23365572 PMC3556443 · DOI
  38. [47]Sturm R. The effects of obesity, smoking, and drinking on medical problems and costs Health Aff. 2002 21 245 253 10.1377/hlthaff.21.2.245 11900166 · DOI
  39. [48]Ding E.L. Song Y. Malik V.S. Liu S. Sex differences of endogenous sex hormones and risk of type 2 diabetes: A systematic review and meta-analysis JAMA 2006 295 1288 1299 10.1001/jama.295.11.1288 16537739 · DOI · Paper page
  40. [50]Eliasson B. Cigarette smoking and diabetes Prog. Cardiovasc. Dis. 2003 45 405 413 10.1016/S0033-0620(03)80004-X 12704597 · DOI
  41. [51]Smola A.J. Schölkopf B. A tutorial on support vector regression Stat. Comput. 2004 14 199 222 10.1023/B:STCO.0000035301.49549.88 · DOI
  42. [52]Jang M. Park D.-C. Application of classifier integration model with confusion table to audio data classification Int. J. Mach. Learn. Comput. 2019 9 368 373 10.18178/ijmlc.2019.9.3.812 · DOI
  43. [53]Tigga N.P. Garg S. Prediction of type 2 diabetes using machine learning classification methods Procedia Comput. Sci. 2020 167 706 716 10.1016/j.procs.2020.03.336 · DOI
  44. [54]Lee Y.-H. Bang H. Kim H.C. Park S.W. Kim D.J. A simple screening score for diabetes for the korean population: Development, validation, and comparison with other scores Diabetes Care 2012 10.2337/dc11-2347 22688547 PMC3402268 · DOI

Cited by

Sorted by citations

Authors
Deberneh, H. M., & Kim, I.
Affiliations
Myongji University (KR)
Corresponding author
Kim, Intaek
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2021-03-23
Volume
18
Issue
6
Pages
3317
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph18063317
PMC
PMC8004981
OpenAlex
W3137532457
Cited by
261
References
3
Citations by year
2026: 45 · 2025: 61 · 2024: 64 · 2023: 54 · 2022: 32 · 2021: 5
FWCI
33.78
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
type 2 diabetes prediction, diabetes risk prediction, prediabetes, electronic health records, feature selection, ensemble learning, XGBoost, random forest, support vector machine, logistic regression, fasting blood glucose, HbA1c, Korean population
MeSH terms
Machine Learning, Algorithms, Diabetes Mellitus, Type 2, Humans, Prediabetic State, Logistic Models

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