An analysis of computational intelligence techniques for diabetes prediction

  • Authors

    • A. K.M. Sazzadur Rahman Department of Computer Science & Engineering, Daffodil International University, Dhaka
    • Md. Mehedi Hasan Department of Software Engineering, Daffodil International University,Dhaka
    • Md. Asaduzzaman Department of Computer Science & Engineering, Daffodil International University, Dhaka
    • Syed Akhter Hossain Department of Computer Science & Engineering, Daffodil International University, Dhaka
    2019-05-27
    https://doi.org/10.14419/ijet.v7i4.28245
  • Machine Learning, Classification, Disease Prediction, Diabetes.

  • Abstract

    Most of the time early detection and diagnosis of diabetes are very costly and complicated. The main objective of this study is to evaluate the performance of different Machine Learning algorithms in order to reduce the cost of the treatment. Considering diabetes, early prediction of diabetes is an important issue in Health Care Services (HCS). So, there is a need for an application that can effectively diagnosis thousands of patients using medical specifications. In this work, we examine different machine learning algorithms for predicting diabetes in real time by drawing from ideas and techniques in the field of machine learning. This study used 4 classification techniques for diabetes prediction. Such as, Artificial Neural Network (ANN), Random Forest (RF), Naive Bayes (NB) and Support Vector Machine (SVM). The performance of different classification techniques was evaluated on different measurement techniques. Moreover, the present study mainly focusses on the use of medical code data for disease prediction and explore different ways for representing such data in my prediction algorithms.

     

     

  • References

    1. [1] How Many People Have Diabetes?, [Online].Available:https://www.diabetesdaily.com/learn-about-diabetes/what-is-diabetes/how-many-people-have-diabetes/. [Accessed: 08-Jun-2018].

      [2] Diabetes, 2017, [Online]. Available:http://www.who.int/news-room/fact-sheets/detail/diabetes. [Accessed: 08-Jun-2018].

      [3] Research Summary | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), [Online]. Available: https://www.niddk.nih.gov/about-niddk/staffdirectory/intramural/leslie-baier/Pages/researchsummary.aspx. [Accessed: 08-Jun-2018].

      [4] A. L. Tarca, V. J. Carey, X. Chen, R. Romero, and S. Drăghici, Machine Learning and Its Applications to Biology, PLoS Comput. Biol., 2007, vol. 3, no. 6, p. e116. https://doi.org/10.1371/journal.pcbi.0030116.

      [5] M. van Gerven and S. Bohte, Editorial: Artificial Neural Networks as Models of Neural Information Processing, Front. Comput. Neurosci., Dec. 2017, vol. 11, p. 114. https://doi.org/10.3389/fncom.2017.00114.

      [6] N. R. Hecht. Theory of the backpropagation neural network. In International Joint Conference on Neural Networks, 1989, June, (Vol. 2, pp. 593-605).

      [7] L. Breiman, Random Forests, Mach. Learn., 2001, vol. 45, no. 1, pp. 5–32. https://doi.org/10.1023/A:1010933404324.

      [8] K. M. Leung, Naive bayesian classifier, Polytech. Univ. Dep. Comput. Sci. Risk Eng., 2007.

      [9] V. Vapnik, I. Guyon, T. H.-M. Learn, and undefined 1995, Support vector machines, statweb.stanford.edu.

      [10] A. Y. Chervonenkis, Early History of Support Vector Machines, in Empirical Inference, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 13–20. https://doi.org/10.1007/978-3-642-41136-6_3.

      [11] I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, Machine Learning and Data Mining Methods in Diabetes Research, Comput. Struct. Biotechnol. J., Jan. 2017, vol. 15, pp. 104–116. https://doi.org/10.1016/j.csbj.2016.12.005.

      [12] D. Sisodia and D. S. Sisodia, Prediction of Diabetes using Classification Algorithms, Procedia Comput. Sci., Jan. 2018, vol. 132, pp. 1578–1585. https://doi.org/10.1016/j.procs.2018.05.122.

      [13] P. Samant and R. Agarwal, Machine learning techniques for medical diagnosis of diabetes using iris images, Comput. Methods Programs Biomed., Apr. 2018, vol. 157, pp. 121–128. https://doi.org/10.1016/j.cmpb.2018.01.004.

      [14] B. López, F. Torrent-Fontbona, R. Viñas, and J. M. Fernández-Real, Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction, Artif. Intell. Med., Apr. 2018, vol. 85, pp. 43–49. https://doi.org/10.1016/j.artmed.2017.09.005.

      [15] F. Mercaldo, V. Nardone, and A. Santone, Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques, Procedia Comput. Sci., Jan. 2017, vol. 112, pp. 2519–2528. https://doi.org/10.1016/j.procs.2017.08.193.

      [16] M. Nilashi, O. bin Ibrahim, H. Ahmadi, and L. Shahmoradi, An analytical method for diseases prediction using machine learning techniques, Comput. Chem. Eng., Nov. 2017, vol. 106, pp. 212–223. https://doi.org/10.1016/j.compchemeng.2017.06.011.

      [17] M. Maniruzzaman et al., Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm, Comput. Methods Programs Biomed., Dec. 2017,vol. 152, pp. 23–34. https://doi.org/10.1016/j.cmpb.2017.09.004.

      [18] T. Zheng et al., A machine learning-based framework to identify type 2 diabetes through electronic health records, Int. J. Med. Inform., Jan. 2017, vol. 97, pp. 120–127. https://doi.org/10.1016/j.ijmedinf.2016.09.014.

      [19] H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, Type 2 diabetes mellitus prediction model based on data mining, Informatics Med. Unlocked, Jan. 2018, vol. 10, pp. 100–107. https://doi.org/10.1016/j.imu.2017.12.006.

      [20] D. Jain and V. Singh, Feature selection and classification systems for chronic disease prediction: A review, Egypt. Informatics J. Apr. 2018. https://doi.org/10.1016/j.eij.2018.03.002.

      [21] L. Tapak, N. Shirmohammadi-Khorram, P. Amini, B. Alafchi, O. Hamidi, and J. Poorolajal, Prediction of survival and metastasis in breast cancer patients using machine learning classifiers, Clin. Epidemiol. Glob. Heal., Oct. 2018. https://doi.org/10.1016/j.cegh.2018.10.003.

      [22] J. T. Senders et al., Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review, World Neurosurg., Jan. 2018, vol. 109, p. 476–486.e1. https://doi.org/10.1016/j.wneu.2017.09.149.

      [23] S. M Mahmud. et al.,Machine Learning Based Unified Framework for Diabetes Predic-tion. Proceedings of the 2018 International Conference on Big Data Engineering and Tech-nology. ACM, 2018.

      [24] A.K.Dwivedi, Analysis of computational intelligence techniques for diabetes mellitus prediction. Neural Comput Appl 1–9 . 2017. https://doi.org/10.1007/s00521-017-2969-9.

      [25] M. R. Ahmed, et al. “A literature review on NoSQL database for big data processing,†Int. J. Eng. Technol., 2018, vol. 7, no. 2, pp. 902–906. https://doi.org/10.14419/ijet.v7i2.12113.

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  • How to Cite

    K.M. Sazzadur Rahman, A., Mehedi Hasan, M., Asaduzzaman, M., & Akhter Hossain, S. (2019). An analysis of computational intelligence techniques for diabetes prediction. International Journal of Engineering & Technology, 7(4), 6229-6232. https://doi.org/10.14419/ijet.v7i4.28245

    Received date: 2019-03-06

    Accepted date: 2019-03-29

    Published date: 2019-05-27