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Prediction of type II diabetes mellitus based on demographic features by the use of machine learning classification algorithms — a study across Assam, India

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International Journal of Diabetes in Developing Countries Aims and scope Submit manuscript

Abstract

Background

The incidence of type II diabetes mellitus (T2DM) has quadruplicated in the recent decades and Prevention of T2DM cases is possible by changing lifestyle practices. The process of diagnosis of diabetes is a tedious one. The advent and advancement in (AI) techniques presents a probable solution to this critical problem.

Objective

The study aims to assess the diverse attributes of the test sample population across Assam and enhance the early prediction of Type II Diabetes Mellitus by employing artificial neural networks.

Methods

The aim of this study is to design a suitable AI model that prognosticates the likelihood of diabetes in individuals with maximum accuracy based on the levels of liver enzymes. This work also analyzes the effect of fast food intake, sleeping patterns, and consumption of alcohol on healthy controls and contemplates their susceptibility to contract T2DM.

Results

The AI model accurately predicted T2DM likelihood and revealed significant links between unhealthy behaviors and increased T2DM risk among healthy individuals.

Conclusions

The study underscores lifestyle modifications for T2DM prevention, highlighting AI’s potential in diagnosis and the impact of unhealthy habits on T2DM susceptibility.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Rafaqat S, Sattar A, Khalid A, Rafaqat S. Role of liver parameters in diabetes mellitus - a narrative review. Endocr Regul. 2023;57(1):200–20.

    Article  PubMed  Google Scholar 

  2. Li Y, Wang J, Han X, Hu H, Wang F, Yu C, Yuan J, Yao P, Li X, Yang K, Miao X, Wei S, Wang Y, Chen W, Liang Y, Zhang X, Guo H, Yang H, Wu T, He M. Serum alanine transaminase levels predict type 2 diabetes risk among a middle-aged and elderly Chinese population. Ann Hepatol. 2019;18:298–303.

    Article  CAS  PubMed  Google Scholar 

  3. Abdu Y, Naja S, Mohamed Ibrahim MI, Abdou M, Ahmed R, Elhag S, Saleh AO, Yassin M, Bougmiza I. Sleep quality among people with type 2 diabetes mellitus during COVID-19 pandemic: evidence from Qatar’s National Diabetes Center. Diabetes Metab Syndr Obes: Targets Ther. 2023;16:2803–12.

    Article  Google Scholar 

  4. Charan J, Biswas T. How to calculate sample size for different study designs in medical research? Indian J Psychol Med. 2013;35(2):121–6.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Yang J, Zhang K, Xi Z, et al. Short sleep duration and the risk of nonalcoholic fatty liver disease/metabolic associated fatty liver disease: a systematic review and meta-analysis. Sleep Breath. 2023;27:1985–96.

    Article  PubMed  Google Scholar 

  6. Fakhari S, Waszkiewicz N. Old and new biomarkers of alcohol abuse: narrative review. J Clin Med. 2023;12(6):2124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gu X, Drouin-Chartier J-P, Sacks FM, Hu FB, Rosner B, Willett WC. Red meat intake and risk of type 2 diabetes in a prospective cohort study of United States females and males. Am J Clin Nutr. 2023;118(6):1153–63.

    Article  PubMed  Google Scholar 

  8. Zhang Y, Meng Y, Wang J. Higher adherence to plant-based diet lowers type 2 diabetes risk among high and non-high cardiovascular risk populations: a cross-sectional study in Shanxi, China. Nutrients. 2023;15(3):786.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mathur S, Mehta DK, Kapoor S, Yadav S. Liver function in type-2 diabetes mellitus patients. Int J Sci Stud. 2016;3:43–7.

    Google Scholar 

  10. Byrne TJ, Parish JM, Somers V, Aqel BA, Rakela J. Evidence for liver injury in the setting of obstructive sleep apnea. Ann Hepatol. 2012;11:228–31.

    Article  CAS  PubMed  Google Scholar 

  11. Prati D, Taioli E, Zanella A. Updated definitions of healthy ranges for serum alanine aminotransferase levels. Ann Intern Med. 2002;137:1–10.

    Article  CAS  PubMed  Google Scholar 

  12. Li J, Sun C, Liu S, Li Y. Dietary Protein Intake and Type 2 Diabetes Among Women and Men in Northeast China. Sci Rep. 2016;6:37604. https://doi.org/10.1038/srep37604

  13. Das SJ, Dutta S, Tiwari D, Basumatary TK, Kashyap N, Kalita MP, Bose S, Bose PD. Prognostic value of myeloid differentiation primary response protein 88 in type II diabetes mellitus in non-obese NAFLD: a case-control study from Assam. Hum Gene. 2024;39:201246. https://doi.org/10.1016/j.humgen.2023.201246.

    Article  CAS  Google Scholar 

  14. Haykin S. Neural networks: a comprehensive foundation. New Jersey: Prentice Hall PTR; 1998.

    Google Scholar 

  15. Gonem S, Wall A, De P. Prevalence of abnormal liver function tests in patients with diabetes mellitus. Endocrine Abstr. 2007;13:175.

    Google Scholar 

  16. Clark JM, Brancati FL, Diehl AM. The prevalence and etiology of elevated aminotransferase levels in the United States. Am J Gastroenterol. 2003;98:960–7.

    Article  CAS  PubMed  Google Scholar 

  17. Friedman LS, Dienstag JL, Watkins E. Evaluation of blood donors with elevated serum alanine aminotransferase levels. Ann Intern Med. 1987;107:137–44.

    Article  CAS  PubMed  Google Scholar 

  18. Erbey JR, Silberman C, Lydick E. Prevalence of abnormal serum alanine aminotransferase levels in obese patients and patients with type 2 diabetes. Am J Med. 2000;109:588–90.

    Article  CAS  PubMed  Google Scholar 

  19. Lebovitz H, Kreider M, Freed M. Evaluation of liver function in type 2 diabetic patients during clinical trials: evidence that rosiglitazone does not cause hepatic dysfunction. Diabetes Care. 2002;25:815–21.

    Article  CAS  PubMed  Google Scholar 

  20. Ioja S, Weir ID, Rennert NJ. Relationship between sleep disorders and the risk for developing type 2 diabetes mellitus. Postgrad Med. 2012;124:119–29.

    Article  PubMed  Google Scholar 

  21. Feng SZ, Tian JL, Zhang Q, Wang H, Sun N, et al. An experimental research on chronic intermittent hypoxia leading to liver injury. Sleep Breath. 2011;15:493–502.

    Article  PubMed  Google Scholar 

  22. Duffey KJ, Popkin BM. Adults with healthier dietary patterns have healthier beverage patterns. J Nutr. 2006;136:2901–7.

    Article  CAS  PubMed  Google Scholar 

  23. Pamidi S, Tasali E. Obstructive sleep apnea and type 2 diabetes: is there a link? Front Neurol. 2012;3:126. https://doi.org/10.3389/fneur.2012.00126

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Acknowledgments

We sincerely acknowledge the Department of Medicine, Assam Medical College, Dibrugarh, Assam, India, for providing us the data sets pertaining to this study. The data was acquired under the supervision of registered medical practitioners with informed consent of the patients and will be produced if required/asked.

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Authors and Affiliations

  1. Faculty of Engineering, Assam Downtown University, Guwahati, Assam, 781026, India

    Partha Pratim Sarkar

  2. Department of Molecular Biology and Biotechnology, Cotton University, Guwahati, Assam, 781003, India

    Snigdha Jyoti Das

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  1. Partha Pratim Sarkar
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Corresponding author

Correspondence to Partha Pratim Sarkar.

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Ethics approval

Ethical approval from the Institutional Ethics Committee of Gauhati Medical College and Cotton University has been taken and will be provided if required.

Patient consent

Required consent has been duly taken and will be provided if required.

IEC number for Gauhati Medical College and Hospital: MCI/190/2007/Pt-II/Oct.2022/44

IEC number for Cotton University: CU/ACA/ETHICS/2022/1

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The authors declare no competing interests.

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Sarkar, P.P., Das, S.J. Prediction of type II diabetes mellitus based on demographic features by the use of machine learning classification algorithms — a study across Assam, India. Int J Diabetes Dev Ctries (2024). https://doi.org/10.1007/s13410-024-01334-4

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