Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering

  • Md Tahsir Ahmed MunnaEmail author
  • Mirza Mohtashim Alam
  • Shaikh Muhammad Allayear
  • Kaushik Sarker
  • Sheikh Joly Ferdaus Ara
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)


In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes; is one of the most common chronic diseases which is happened to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. Type-2 diabetes is considered one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with type-2 diabetes mellitus. The processing and statistical analysis we used Scikit-Learn, Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge type-2 diabetes mellitus chronic diseases.


Healthcare Machine learning Artificial Neural Network (ANN) Diabetes type-2 Prediction K-means clustering Classification model 


  1. 1.
    Arena, J.G.: Behavioral medicine consulation. In: Handbook of Clinical Interviewing with Adults, p. 446 (2007)Google Scholar
  2. 2.
    Mahmoodi, M., Hosseini-zijoud, S.M., Hassanshahi, G.H., Nabati, S., Modarresi, M., Mehrabian, M., Sayyadi, A., Hajizadeh, M.: J. Diabetes Endocrinol. 4(1), 1–5 (2013). ISSN 2141-2685, Academic JournalGoogle Scholar
  3. 3.
    What is Diabetes? (n.d.). Accessed 28 Aug 2017
  4. 4.
    The State of Diabetes in Bangladesh, 05 October 2016. Accessed 28 Aug 2017
  5. 5.
    Vaz, N.C., Ferreira, A.M., Kulkarni, M.S., Vaz, F.S., Pintondian, N.R.: Prevalence of diabetic complications in rural Goa. India. J Commun. Med. 36(4), 283–286 (2011). Scholar
  6. 6.
    Cao, H.B., Liu, P.A., Jiang, X.G., Jiang, Y.Y., Wang, J.P., Zheng, H., Zhang, H., Bennett, P.H., Howard, B.V.: Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and diabetes study. Diabetes Care 20, 537–544 (1997)CrossRefGoogle Scholar
  7. 7.
    Yorozu, Y., Hirano, M., Oka, K., Tagawa, Y.: Electron spectroscopy studies on magneto-optical media and plastic substrate interface. IEEE Transl. J. Magn. Japan 2, 740–741 (1987). [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982]CrossRefGoogle Scholar
  8. 8.
    Rajesh, K., Sangeetha, V.: Application of data mining methods and techniques for diabetes diagnosis. Int. J. Eng. Innov. Technol. 2(3), 224–229 (2012)Google Scholar
  9. 9.
    Wang, C., Li, L., Wang, L., Ping, Z., Flory, M.T., Wang, G., Li, W.: Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach. Diabetes Res. Clin. Pract. 100(1), 111–118 (2013)CrossRefGoogle Scholar
  10. 10.
    Smith, A.E., Nugent, C.D., McClean, S.I.: Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example. Artif. Intell. Med. 27(1), 1–27 (2003)CrossRefGoogle Scholar
  11. 11.
    Lin, C.S., Chiu, J.S., Hsieh, M.H., Mok, M.S., Li, Y.C., Chiu, H.W.: Predicting hypotensive episodes during spinal anesthesia with the application of artificial neural networks. Comput. Methods Prog. Biomed. 92(2), 193–197 (2008)CrossRefGoogle Scholar
  12. 12.
    Wolk, R., Berger, P., Lennon, R.J., Brilakis, E.S., Somers, V.K.: Body mass index. Circulation 108(18), 2206–2211 (2003)CrossRefGoogle Scholar
  13. 13.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2006)CrossRefGoogle Scholar
  14. 14.
    Garreta, R., Moncecchi, G.: Learning Scikit-Learn: Machine Learning in Python. Packt Publishing Ltd., Birmingham (2013)Google Scholar
  15. 15.
    Hackeling, G.: Mastering Machine Learning with Scikit-Learn. Packt Publishing Ltd., Birmingham (2014)Google Scholar
  16. 16.
    Guo, C., Berkhahn, F.: Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737 (2016)
  17. 17.
    Principe, J.C., Fancourt, C.L.: Artificial neural networks. In: Handbook of Global Optimization, vol. 2, pp. 363–386 (2013)Google Scholar
  18. 18.
    Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–460 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Md Tahsir Ahmed Munna
    • 1
    Email author
  • Mirza Mohtashim Alam
    • 1
  • Shaikh Muhammad Allayear
    • 1
  • Kaushik Sarker
    • 1
  • Sheikh Joly Ferdaus Ara
    • 2
  1. 1.Department of Multimedia and Creative Technology, Department of Software EngineeringDaffodil International UniversityDhakaBangladesh
  2. 2.Department of Microbiology and ImmunologyBangabandhu Sheikh Mujib Medical UniversityDhakaBangladesh

Personalised recommendations