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Diabetes Mellitus Risk Factor Prediction Through Resampling and Cost Analysis on Classifiers

  • S. Poonkuzhali
  • J. JeyalakshmiEmail author
  • S. Sreesubha
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 836)

Abstract

With increasing change in sedentary life style and food habits, diabetes is greatly becoming a bane. It is becoming very critical disease in India with more than 62 million diabetic individuals currently diagnosed with the disease. It is also a growing issue throughout the world. But with modern techniques for analysis, it is very much possible to predict the disease very early and control it by pervasive care. Data Mining techniques and Predictive Analysis are focussed in the proposed system. When select techniques are used for classification after pre-processing the data, along with attribute selection and, it is found that the performance of the classifiers is good. The system presents correlation and correspondence analysis over the attributes in the dataset, a comparative analysis amid several classifiers under the experimental environment in order to propose an efficient hybrid classifier to provide precise prediction over the disease.

Keywords

Data mining Diabetes Hybrid classifier Predictive analytics Pervasive healthcare 

Notes

Acknowledgment

This research work is a part of the All India Council for Technical Education (AICTE), India funded Research Promotion Scheme project titled “Efficient Prediction and Monitoring Tool for Diabetes Patients Using Data Mining and Smart Phone System” with Reference No: 8- 169/RIFD/RPS/POLICY-1/2014-15.

References

  1. 1.
    Smita, P.S.: Use of data mining in various field: a survey paper. IOSR J. Comput. Eng. (IOSR-JCE) 16(3), 18–22 (2014)CrossRefGoogle Scholar
  2. 2.
    Tomar, D., Agarwal, S.: A survey on data mining approaches for healthcare. Int. J. Bio-Sci. Bio-Technol. 5(5), 241–266 (2013)CrossRefGoogle Scholar
  3. 3.
    Chaurasia, V., Pal, S.: Early prediction of heart diseases using data mining techniques. Carib. J. Sci. Tech. 1, 208–217 (2013)Google Scholar
  4. 4.
    Fanga, L., et al.: Feature selection method based on mutual information and class separability for dimension reduction in multidimensional time series for clinical data. Biomed. Sig. Process. Control 21, 82–89 (2015)CrossRefGoogle Scholar
  5. 5.
    Halper, F.: Predictive Analytics for Business Advantage. TDWI Research, pp. 1–32 (2014)Google Scholar
  6. 6.
    Iyer, A., Jeyalatha, S., Sumbaly, R.: Diagnosis of diabetes using classification mining techniques. Int. J. Data Mining Knowl. Manag. Process 5(1), 1–14 (2015)Google Scholar
  7. 7.
    Juliyet, L.C., Amanullah, M.K.: The surveillance on diabetes diagnosis using data mining techniques. Int. J. Sci. Res. Technol. 1(4), 34–39 (2015)Google Scholar
  8. 8.
    Nagarajan, S., Chandrasekaran, R.M., Ramasubramanian, P.: Data mining techniques for performance evaluation of diagnosis in gestational diabetes. Int. J. Curr. Res. Acad. Rev. 2(10), 91–98 (2014)Google Scholar
  9. 9.
    Butwall, M., Kumar, S.: A data mining approach for the diagnosis of diabetes mellitus using random forest classifier. Int. J. Comput. Appl. 120(8), 36–39 (2015)Google Scholar
  10. 10.
    Thirumal, P.C., Nagarajan, N.: Utilization of data mining techniques for diagnosis of diabetes mellitus - a case study. Asian Res. Publ. Netw.-J. Eng. Appl. Sci. 10(1), 8–13 (2015)Google Scholar
  11. 11.
    Sen, S.K., Dash, S.: Application of meta learning algorithms for the prediction of diabetes disease. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2(12), 396–401 (2014)Google Scholar
  12. 12.
    Shivakumar, B.L., Alby, S.: A survey on data-mining technologies for prediction and diagnosis of diabetes. In: International Conference on Intelligent Computing Applications, pp. 167–173 (2014)Google Scholar
  13. 13.
    Visalatchi1, G., Gnanasoundhari, S.J., Balamurugan, M.: A survey on data mining methods and techniques for diabetes mellitus. Int. J. Comput. Sci. Mob. Appl. 2(2), 100–105 (2014)Google Scholar
  14. 14.
    Venkatalakshmi, B., Shivsankar, M.V.: Heart disease diagnosis using predictive data mining. Int. J. Innov. Res. Sci. Eng. Technol. 3(3), 1873–1877 (2014)Google Scholar
  15. 15.
    Agrawal, P., Dewangan, A.K.: A brief survey on the techniques used for the diagnosis of diabetes-mellitus. Int. Res. J. Eng. Technol. 2(3), 1039–1043 (2015)Google Scholar
  16. 16.
    Deepika, N., Poonkuzhali, S.: Design of hybrid classifier for prediction of diabetes through feature relevance analysis. Int. J. Innov. Sci. Eng. Technol. 2(10), 788–793 (2015)Google Scholar
  17. 17.
    Wang, S., Minku, L.L., Yao, X.: Resampling-based ensemble methods for online class imbalance learning. IEEE Trans. Knowl. Data Eng. 26, 1–13 (2014)CrossRefGoogle Scholar
  18. 18.
    Poonkuzhali, S., Sindhuja, M., Jeyalakshmi, J., Sreesubha, S.: Diabetes mellitus risk factor prediction through feature relevance analysis and hybrid classifier. In: Advances in Innovative Engineering and Technologies - Proceedings of the International Conference on Innovative Engineering and Technologies, pp. 317–332 (2016)Google Scholar
  19. 19.
    Yousefi, L., Saachi, L., Bellazzi, R., Chiovato, L., Tucker, A.: Predicting comorbidities using resampling and dynamic bayesian networks with latent variables. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, pp. 205–206 (2017)Google Scholar
  20. 20.
    Hashi, E.K., Zaman, M.S.U., Hasan, M.R.: An expert clinical decision support system to predict disease using classification techniques. In: 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, pp. 396–400 (2017)Google Scholar
  21. 21.
    Vaishali, R., Sasikala, R., Ramasubbareddy, S., Remya, S., Nalluri, S.: Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset. In: 2017 International Conference on Computing Networking and Informatics (ICCNI), Lagos, pp. 1–5 (2017)Google Scholar
  22. 22.
    Negi, A., Jaiswal, V.: A first attempt to develop a diabetes prediction method based on different global datasets. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, pp. 237–241 (2016)Google Scholar
  23. 23.
    Jung, M.: Toward designing mobile software to predict hypoglycemia for patients with diabetes. In: 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), Austin, TX, pp. 29–30 (2016)Google Scholar
  24. 24.
    Jankovic, M.V., Mosimann, S., Bally, L., Stettler, C., Mougiakakou, S.: Deep prediction model: the case of online adaptive prediction of subcutaneous glucose. In: 2016 13th Symposium on Neural Networks and Applications (NEUREL), Belgrade, pp. 1–5 (2016)Google Scholar
  25. 25.
    Botros, F.S., Taher, M.F., ElSayed, N.M., Fahmy, A.S.: Prediction of diabetic foot ulceration using spatial and temporal dynamic plantar pressure. In: 2016 8th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, pp. 43–47 (2016)Google Scholar
  26. 26.
    LaPierre, N., Rahman, M.A., Rangwala, H.: CAMIL: clustering and assembly with multiple instance learning for phenotype prediction. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, pp. 33–40 (2016)Google Scholar
  27. 27.
    Salekin, A., Stankovic, J.: Detection of chronic kidney disease and selecting important predictive attributes. In: 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, IL, pp. 262–270 (2016)Google Scholar
  28. 28.
    Sheng, K., Liu, Z., Zhou, D.: An adaptive resampling algorithm based on CFSFDP. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), Beijing, China, pp. 41–45 (2017)Google Scholar
  29. 29.
    Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the kappa statistic. Fam. Med. 37(5), 360–363 (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyRajalakshmi Engineering CollegeChennaiIndia

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