Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
American Diabetes Association: Diagnosis & classification of diabetes mellitus. Diabetes Care 37(Supplement 1), S81–S90 (2014)
American Diabetes Association: Reports of the experts committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 23, S4–19 (2001)
National Diabetes Information clearinghouse (NDIC): http://diabetes.niddk.nih.gov
World Health Organization (WHO): Diabetes. 30 October 2018. http://www.who.int/mediacentre/factsheets/fs312/en/
Contreras, I., Vehi, J.: Artificial intelligence for diabetes management and decision support: literature review. J. Med. Internet Res. 20, e10775 (2018)
CB Insights: This is how artificial intelligence is transforming diabetes care management. https://www.cbinsights.com/research/?s=diabetes+
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)
Shankaracharya, D.O., Samanta, S., Vidyarthi, A.S.: Computational intelligence in early diabetes diagnosis: a review. Rev. Diabet. Stud. 7, 252–262 (2010)
Shanker, M.S.: Using neural networks to predict the onset of diabetes mellitus. J. Chem. Inf. Comput. Sci. 36, 35–41 (1996)
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)
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: 2321-0869
Kandhasamy, J.P., Balamurali, S.: Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput. Sci. 47, 45–51 (2015)
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)
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)
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)
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)
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)
Shital, T., Madan, S., Pranjali, C., Swati, S.: SVM based diabetic classification and hospital recommendation. Int. J. Comput. Appl. 167(1), 40–43 (2017)
Barale, M.S., Shirke, D.T.: Cascaded modeling for PIMA Indian diabetes data. Int. J. Comput. Appl. 139(11), 1–4 (2016)
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)
Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back propagation for classification. Int. J. Comput. Theory Eng. 3(1), 1793–8201 (2011)
Shantakumar, B.P., Kumarasawamy, S.: Predictive data mining for medical diagnosis of heart disease 1(2), 161–176 (2009)
http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
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 2016
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kriještorac, M., Halilović, A., Kevric, J. (2020). The Impact of Predictor Variables for Detection of Diabetes Mellitus Type-2 for Pima Indians. In: Avdaković, S., Mujčić, A., Mujezinović, A., Uzunović, T., Volić, I. (eds) Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019). IAT 2019. Lecture Notes in Networks and Systems, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-24986-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-24986-1_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24985-4
Online ISBN: 978-3-030-24986-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)