The Diagnosis of Diabetes Using a Hybrid Algorithm Consisting of the Flower Pollination Algorithm and an Ensemble of a Subset of K-NN Classifiers

Document Type : Original Article

Authors

1 computer science Kosar university of Bojnord

2 computer Kosar university of Bojnord

Abstract

Diabetes is a disease which, as well as prevention, requires a high level of care, such as monitoring the blood sugar changes. The timely diagnosis of disease plays an important role in its treatment and decreases the damage caused by the disease. Therefore, it is essential to diagnose diabetes. Since hybrid algorithms have a high ability to predict and diagnose various diseases, this article presents an intelligent approach to the diagnosis of this disease, using a hybrid algorithm of flower pollination and K-nearest neighbor ensemble. The accuracy of the proposed method is measured to be 97.78, by using Pima Indians Diabetes (PID) dataset, consisting of 768 samples and 8 features. The results show that the accuracy of this approach has significantly increased compared with the previous studies, and confirms the superiority of the proposed method.

Keywords

References

  1. Abdelaziz A.Y., Ali E.S., Abd Elazim S.M., (2016), Flower pollination algorithm to solve combined economic and emission dispatch problems, Engineering Science and Technology, an International Journal, 19: 980–990 DOI:10.1007/s10287-009-0113-8

    Aiswarya I., S. Jeyalatha and Ronak S., (2015), Diagnosis of Diabetes Using Classification Mining Techniques”, International Journal of Data Mining & Knowledge Management Process (IJDKP),5(1): 1-14 DOI:10.1007/s10287-009-0113-8

    Alka L., Dharmender K., (2016), Survey on KNN and Its Variants, IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, 5(5): 430-435. DOI:10.1109/CEC.2005.1554852

    Anuja V. and Chitra R., (2013), Classification Of Diabetes Disease Using Support Vector Machine”, International Journal of Engineering Research and Applications (IJERA), 3(2): 1797-1801 DOI:10.1016/j.ejor.2006.12.024

    Asma G., Aris P., Zardad K., Osama M., Miftahuddin M., Werner A., Berthold L., (2016), Ensemble of a subset of kNN classifiers, Mathematics Subject Classification, 1-14. Doi: 10.22091/jemsc.2019.1294

    Bhuvaneswari P., Brintha A., (2015), Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm. Procedia Materials Science. 10: 433 – 440. DOI:10.1016/j.cor.2005.06.017

    Francisco J.C., Jose J.V., Jorge C.,

    Juan R.R., (2018), Oversampling imbalanced data in the string space, Pattern Recognition Letters,103: 32-38. DOI:10.1016/j.eswa.2011.03.060

    1. Krishnaveni, T. Sudha, (2017) A Novel Technique to Predict Diabetic Disease Using Data Mining Classification Techniques” in International Conference on Innovative Applications in Engineering and Information Technology (ICIAEIT- 2017), 3(1): 5-11 Doi: 10.22091/jemsc.2019.1294

    Harleen and Bhambri P, (2016), A Prediction Technique in Data Mining for Diabetes Mellitus,” Journal of Management Sciences and Technology, 4(1): 1-12. Doi: 10.22091/jemsc.2019.1294

    Haruna C., Liyana M.S., Sanah A.M., Adamu I. A., et al, (2015), A Review of the Applications of Bio-Inspired Flower Pollination Algorithm, The 2015 International Conference on Soft Computing and Software Engineering (SCSE 2015), 62:  435-441 Doi: 10.22091/jemsc.2019.1294

    Ihsan S., Osman N., Oguz B. and Khalid S., (2018), Impact of Metaheuristic Iteration on Artificial Neural Network Structure in Medical Data, Processes, 6, 57. DOI:10.1109/TEVC.2008.925798

    Lenin K. and Reddy B. R., (2014), Hybrid Eagle Strategy Flower Pollination Algorithm for Solving Optimal Reactive Power Dispatch Problem, International Journal of Electrical Energy, 2(3)  DOI:10.1016/j.sbspro.2013.03.036

    Lichman, M. UCI Machine Learning Repository; University of California, School of Information and Computer Science: Irvine, CA, USA, http://www.ics.uci.edu/ ∼ mlearn/MLRepository.html DOI:10.1016/j.amc.2003.10.057

    Murali V., and George S. )2007(. An overview of internet addiction. Advances in Psychiatric Treatment, 13: 24-30. Doi:  10.1016/j.proeng.2013.04.103

    Yilmaz N., Inan O., Uzer M.S, (2014), A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases,” J Med Syst, 38(5): 38-48. DOI:10.1007/BF02282055

    Zheng. Z. )2015(. Oversampling method for imbalanced classification computing and Informatics, 34: 1017–1037. Doi: 10.1002/nav.3800030110

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