Diagnosis of Diabetes Using Bee Colony Algorithm and Fuzzy Decision Tree

M. Mojarad¹ , E. Hajizadegan² , M. Gurkani³

Section:Research Paper, Product Type: Journal
Vol.9 , Issue.3 , pp.16-21, Jun-2021

Online published on Jun 30, 2021

Copyright © M. Mojarad, E. Hajizadegan, M. Gurkani . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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How to Cite this Paper

Abstract :
M. Mojarad1*, E. Hajizadegan2, M. Gurkani2

Key-Words / Index Term :
Diagnosis of diabetes, bee colony algorithm, fuzzy decision tree

References :
[1] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 15, 104-116.
[2] Rezaeipanah, A., Mojarad, M., & Sechin Matoori, S. (2021). Intrusion Detection in Computer Networks Through Combining Particle Swarm Optimization and Decision Tree Algorithms. Journal of Business Data Science Research, 1(1), 14-22.
[3] Rezaeipanah, A., & Ahmadi, G. (2020). Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network. The Computer Journal.
[4] Selvakumar, S., Kannan, K. S., & GothaiNachiyar, S. (2017). Prediction of diabetes diagnosis using classification based data mining techniques. International Journal of Statistics and Systems, 12(2), 183-188.
[5] Ghalehgolabi, M., & Rezaeipanah, A. (2017). Intrusion detection system using genetic algorithm and data mining techniques based on the reduction. International Journal of Computer Applications Technology and Research, 6(11), 461-466.
[6] Jain, V., & Raheja, S. (2015). Improving the prediction rate of diabetes using fuzzy expert system. IJ Information Technology and Computer Science, 7(10), 84-91.
[7] Choubey, D. K., Paul, S., Kumar, S., & Kumar, S. (2017, February). Classification of Pima Indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection. In Communication and Computing Systems: Proceedings of the International Conference on Communication and Computing System (pp. 451-455).
[8] Pradhan, M. A., Bamnote, G. R., Tribhuvan, V., Jadhav, K., Chabukswar, V., & Dhobale, V. (2012). A genetic programming approach for detection of diabetes. Int J Comput Eng Res (ijceronline. com), 2(6), 91.
[9] Saybani, M. R., Shamshirband, S., Golzari, S., Wah, T. Y., Saeed, A., Kiah, M. L. M., & Balas, V. E. (2016). RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system. Medical & biological engineering & computing, 54(2-3), 385-399.
[10] Beloufa, F., & Chikh, M. A. (2013). Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm. Computer methods and programs in biomedicine, 112(1), 92-103.
[11] Ahmad, F., Isa, N. A. M., Hussain, Z., Osman, M. K., & Sulaiman, S. N. (2015). A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer. Pattern Analysis and Applications, 18(4), 861-870.
[12] Pourpanah, F., Lim, C. P., & Saleh, J. M. (2016). A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction. Expert Systems with Applications, 49, 74-85.
[13] Cheruku, R., Edla, D. R., & Kuppili, V. (2017). SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification. Computers in biology and medicine, 81, 79-92.