Advertisement

Neural Computing and Applications

, Volume 30, Issue 12, pp 3837–3845 | Cite as

Analysis of computational intelligence techniques for diabetes mellitus prediction

  • Ashok Kumar DwivediEmail author
Original Article

Abstract

Diabetes as a chronic disease is becoming a foremost community health concern worldwide. In developing countries, the diabetic patients are increasing rapidly due to lack of sentience and bad eating habits. So, there is a need of a framework that can effectively diagnose thousands of patients using clinical specifics. This work uses six computational intelligence techniques for diabetes mellitus prediction namely classification tree, support vector machine, logistic regression, naïve Bayes, and artificial neural network. The performance of these techniques was evaluated on eight different classification performance measurements. Moreover, these techniques were appraised on a receiver operative characteristic curve. Classification accuracy of 77 and 78% was achieved by artificial neural network and logistic regression, respectively, with F 1 measure of 0.83 and 0.84.

Keywords

Classification tree Artificial neural network Naïve Bayes Logistic regression Diabetes mellitus Support vector machine Classification Machine learning algorithm Treatments 

Notes

Acknowledgements

The author is highly grateful to the Department of Biotechnology, New Delhi for providing support for this work under Bioinformatics Infrastructure Facility of Department of Biotechnology, Ministry of Science and Technology, India at Maulana Azad National Institute of Technology, Bhopal.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

References

  1. 1.
    Vapnik VN, Vapnik V (1998) Statistical learning theory. Wiley New York, vol 2Google Scholar
  2. 2.
    King H, Aubert RE, Herman WH (1998) Global burden of diabetes, 1995–2025: prevalence, numerical estimates, and projections. Diabetes Care 21(9):1414–1431CrossRefGoogle Scholar
  3. 3.
    Alberti KGMM, Pf Z (1998) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation Diabetic medicine 15(7):539–553Google Scholar
  4. 4.
    Goswami SK, Vishwanath M, Gangadarappa SK, Razdan R, Inamdar MN (2014) Efficacy of ellagic acid and sildenafil in diabetes-induced sexual dysfunction. Pharmacogn Mag 10(39):581CrossRefGoogle Scholar
  5. 5.
    Goswami SK, Gangadarappa SK, Vishwanath M, Razdan R, Jamwal R, Bhadri N, Inamdar MN (2016) Antioxidant potential and ability of phloroglucinol to decrease formation of advanced glycation end products increase efficacy of sildenafil in diabetes-induced sexual dysfunction of rats. Sex Med 4(2):e104–e112Google Scholar
  6. 6.
    Varma R, Bressler NM, Doan QV, Gleeson M, Danese M, Bower JK, Selvin E, Dolan C, Fine J, Colman S (2014) Prevalence of and risk factors for diabetic macular edema in the United States. JAMA Ophthalmology 132(11):1334–1340CrossRefGoogle Scholar
  7. 7.
    Amiri A, Rafe V (2014) Hybrid algorithm for detecting diabetes. Int Res J Appl Basic Sci 8(12):2347–2353Google Scholar
  8. 8.
    Dwivedi AK (2016) Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput & Applic:1–9Google Scholar
  9. 9.
    Dwivedi AK, Chouhan U (2016) Comparative study of machine learning techniques for genome scale discrimination of recombinant HIV-1 strains. J Med Imaging Health Inform 6(2):425–430CrossRefGoogle Scholar
  10. 10.
    Dwivedi AK, Chouhan U (2014) On support vector machine ensembles for classification of recombination breakpoint regions in Saccharomyces cerevisiae. Int J Comput Appl 108(13)Google Scholar
  11. 11.
    Dwivedi AK, Chouhan U (2016) Genome-scale classification of recombinant and non-recombinant HIV-1 sequences using artificial neural network ensembles. Curr Sci 111(5):853CrossRefGoogle Scholar
  12. 12.
    Farran B, Channanath AM, Behbehani K, Thanaraj TA (2013) Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait—a cohort study. BMJ Open 3(5):e002457CrossRefGoogle Scholar
  13. 13.
    Heydari M, Teimouri M, Heshmati Z, Alavinia SM (2015) Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. International Journal of Diabetes in Developing Countries:1–7Google Scholar
  14. 14.
    Bansal A, Agarwal R, Sharma R (2015) Determining diabetes using iris recognition system. Int J Diabetes Dev Countries 35(4):432–438CrossRefGoogle Scholar
  15. 15.
    Kalaiselvi C, Nasira G Classification and prediction of heart disease from diabetes patients using hybrid particle swarm optimization and library support vector machine algorithm.Google Scholar
  16. 16.
    Bhramaramba R, Allam AR, Kumar VV, Sridhar G (2011) Application of data mining techniques on diabetes related proteins. Int J Diabetes Dev Countries 31(1):22–25CrossRefGoogle Scholar
  17. 17.
    Demouy J, Chamberlain J, Harris M, Marchand L (1995) The Pima Indians: pathfinders of health. Nat. Inst. Diabetes Digestive Kidney Diseases, Bethesda, MDGoogle Scholar
  18. 18.
    Smith JW, Everhart J, Dickson W, Knowler W, Johannes R. (1988) Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Annual Symposium on Computer Application in Medical Care. American Medical Informatics Association, p 261Google Scholar
  19. 19.
    Group NDD (1995) National Institute of Diabetes and Digestive and Kidney Diseases. Diabetes in America, 2nd edition NIH publication (95-1468)Google Scholar
  20. 20.
    García-Pedrajas N, Hervás-Martínez C, Ortiz-Boyer D (2005) Cooperative coevolution of artificial neural network ensembles for pattern classification. Evolutionary Computation, IEEE Transactions on 9(3):271–302CrossRefGoogle Scholar
  21. 21.
    Yao X, Liu Y (1998) Making use of population information in evolutionary artificial neural networks. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 28(3):417–425MathSciNetGoogle Scholar
  22. 22.
    Bishop CM (1995) Neural networks for pattern recognition.Google Scholar
  23. 23.
    Haykin S (2010) Neural networks: a comprehensive foundation, 1994. Mc Millan, New JerseyGoogle Scholar
  24. 24.
    Vapnik V (2000) The nature of statistical learning theory. springerGoogle Scholar
  25. 25.
    Hosmer Jr DW, Lemeshow S (2004) Applied logistic regression. Second edn. John Wiley & Sons, Columbus, OhioGoogle Scholar
  26. 26.
    Schumacher M, Roßner R, Vach W (1996) Neural networks and logistic regression: part I. Comput Stat Data Anal 21(6):661–682CrossRefGoogle Scholar
  27. 27.
    Vach W, Roßner R, Schumacher M (1996) Neural networks and logistic regression: part II. Comput Stat Data Anal 21(6):683–701CrossRefGoogle Scholar
  28. 28.
    Hajmeer M, Basheer I (2003) Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiol 20(1):43–55CrossRefGoogle Scholar
  29. 29.
    Aha DW (1997) Lazy learning. Kluwer academic publishersGoogle Scholar
  30. 30.
    Provost FJ, Fawcett T, Kohavi R (1998) The case against accuracy estimation for comparing induction algorithms. In: ICML, pp 445–453Google Scholar
  31. 31.
    Van Den Bosch A, Weijters A, Van Den Herik HJ, Daelemans W (1997) When small disjuncts abound, try lazy learning: a case study. In: Proceedings of the Seventh Belgian-Dutch Conference on Machine Learning. Citeseer, pp 109–118Google Scholar
  32. 32.
    Shafer G, Pearl J (1990) Readings in uncertain reasoning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USAGoogle Scholar
  33. 33.
    Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243zbMATHGoogle Scholar
  34. 34.
    Jensen FV (1996) An introduction to Bayesian networks, UCL press London, vol 210Google Scholar
  35. 35.
    Peral J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo, Cali fornia 12:241–288Google Scholar
  36. 36.
    Castillo E (1997) Expert systems and probabilistic network models. SpringerGoogle Scholar
  37. 37.
    Kanmani S, Uthariaraj VR, Sankaranarayanan V, Thambidurai P (2007) Object-oriented software fault prediction using neural networks. Inf Softw Technol 49(5):483–492CrossRefGoogle Scholar
  38. 38.
    Metz CE (1978) Basic principles of ROC analysis. In: Seminars in nuclear medicine, Elsevier, vol 4 pp 283–298CrossRefGoogle Scholar
  39. 39.
    Cohen I, Goldszmidt M (2004) Properties and benefits of calibrated classifiers. In: Knowledge Discovery in Databases: PKDD 2004. Springer, pp 125–136Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Bioinformatics, Mathematics and Computer ApplicationsMaulana Azad National Institute of TechnologyBhopalIndia

Personalised recommendations