The Impact of Predictor Variables for Detection of Diabetes Mellitus Type-2 for Pima Indians

  • Maida KriještoracEmail author
  • Alma Halilović
  • Jasmin Kevric
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 83)


Diabetes Mellitus type-2 is one of the diseases of a modern age treated as a serious illness due to its symptoms in later stages, consequences if left untreated and its complexity in terms of detection, diagnosis, and prognosis widely spread among the Pima Indian population. The process of detecting the diabetes will require analysis of the data, processing, extracting portions of data into a set for training, testing and validation sets. Then applying several different machine learning algorithms, train a model, check the performance of the trained model and iterate with other algorithms until we find the most performant for our type of data. The goal of this research is to investigate which algorithm gives best results in terms of detecting the existing disease as well as predicting the possibility of getting one in the future, based on the diagnostic measurements of the patient. For this matter, MATLAB software will be used.


  1. 1.
    American Diabetes Association: Diagnosis & classification of diabetes mellitus. Diabetes Care 37(Supplement 1), S81–S90 (2014)CrossRefGoogle Scholar
  2. 2.
    American Diabetes Association: Reports of the experts committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 23, S4–19 (2001)Google Scholar
  3. 3.
    National Diabetes Information clearinghouse (NDIC):
  4. 4.
    World Health Organization (WHO): Diabetes. 30 October 2018.
  5. 5.
    Contreras, I., Vehi, J.: Artificial intelligence for diabetes management and decision support: literature review. J. Med. Internet Res. 20, e10775 (2018)Google Scholar
  6. 6.
    CB Insights: This is how artificial intelligence is transforming diabetes care management.
  7. 7.
    Ramezankhani, A., Pournik, O., Shahrabi, J., Azizi, F., Hadaegh, F., Khalili, D.: The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes. SAGE J. 36(1), 37–144 (2016)Google Scholar
  8. 8.
    Shankaracharya, D.O., Samanta, S., Vidyarthi, A.S.: Computational intelligence in early diabetes diagnosis: a review. Rev. Diabet. Stud. 7, 252–262 (2010)Google Scholar
  9. 9.
    Shanker, M.S.: Using neural networks to predict the onset of diabetes mellitus. J. Chem. Inf. Comput. Sci. 36, 35–41 (1996)CrossRefGoogle Scholar
  10. 10.
    Purnami, S.W., Embong, A., Zain, J.M.: A new smooth support vector machine and its applications in diabetes disease diagnosis. J. Comput. Sci. 5, 1006–1011 (2009)Google Scholar
  11. 11.
    Jhaldiyal, T., Mishra, P.K.: Analysis and prediction of diabetes mellitus using PCA, REP SVM. Int. J. Eng. Tech. Res. (IJETR) 2(8) (2014) ISSN: 2321-0869Google Scholar
  12. 12.
    Kandhasamy, J.P., Balamurali, S.: Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput. Sci. 47, 45–51 (2015)CrossRefGoogle Scholar
  13. 13.
    Kordos, M., Blachnik, M., Strzempa, D.: Do we need whatever more than k-NN? In: Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, Part I, pp. 414–421. Springer-Verlag, Berlin (2010)Google Scholar
  14. 14.
    Ster, B., Dobnikar, A.: Neural networks in medical diagnosis: comparison with other methods. In: Proceedings of the International Conference on Engineering Applications with Neural Networks, pp. 427–430. London (1996)Google Scholar
  15. 15.
    Jahangeer, S., Zaman, M., Ahmed, M., Ashraf, M.: An empirical comparison of supervised classifiers for diabetic diagnosis. Int. J. Adv. Res. Comput. Sci. 8(1), 311–315 (2017)Google Scholar
  16. 16.
    Polat, K., Gunes, S., Arslan, A.: A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Exp. Syst. Appl. 34, 482–487 (2008)CrossRefGoogle Scholar
  17. 17.
    Christobel, Y.A., Sivaprakasam, P.: Improving the performance of k-nearest neighbor algorithm for the classification of diabetes dataset with missing values IJCET 3(3), 155–167 (2012)Google Scholar
  18. 18.
    Shital, T., Madan, S., Pranjali, C., Swati, S.: SVM based diabetic classification and hospital recommendation. Int. J. Comput. Appl. 167(1), 40–43 (2017)Google Scholar
  19. 19.
    Barale, M.S., Shirke, D.T.: Cascaded modeling for PIMA Indian diabetes data. Int. J. Comput. Appl. 139(11), 1–4 (2016)CrossRefGoogle Scholar
  20. 20.
    Hashi, E.K., Zaman, S.U., Hasan, R.: Developing diabetes disease classification model using sequential forward selection algorithm. Int. J. Comput. Appl. 180(5), 1–6 (2017)Google Scholar
  21. 21.
  22. 22.
    Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back propagation for classification. Int. J. Comput. Theory Eng. 3(1), 1793–8201 (2011)Google Scholar
  23. 23.
    Shantakumar, B.P., Kumarasawamy, S.: Predictive data mining for medical diagnosis of heart disease 1(2), 161–176 (2009)Google Scholar
  24. 24.
  25. 25.
    Nakano, T., Nukala, B.T., Zupancic, S., Rodriguez, A., Lie, D.Y.C., Lopez, J., Nguyen, T.Q.: Gaits classification of normal vs. patients by wireless gait sensor and support vector machine (SVM) classifier. In: IEEE ICIS 2016, Okayama, Japan, June 2016Google Scholar
  26. 26.
    Abdillah, A.A., Suwarno, S.: Diagnosis of diabetes disease using support vector machines with kernel radial basis function. In: Conference on International Conference on Mathematics, Its Applications, and Mathematics Education (ICMAME) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Maida Kriještorac
    • 1
    Email author
  • Alma Halilović
    • 1
  • Jasmin Kevric
    • 1
  1. 1.Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics EngineeringInternational Burch UniversityIlidžaBosnia and Herzegovina

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