Document Type : Original/Review Paper


Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.



This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.


[1] M. Nazarzadeh, Z. Bidel, and A. Sanjari Moghaddam, “Meta-analysis of diabetes mellitus and risk of hip fractures small study effect,” Osteoporos Int, vol. 27, pp. 229, 2016. 
[2] Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019. Results. Institute for Health Metrics and Evaluation. 2020. (
[3] H. Temurtas, N. Yumusak, and F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks,” Expert Systems with Applications, vol. 36, pp. 8610–8615, 2009. 
[4] A. Iyer, S. Jeyalatha, and R. Sumbaly, "Diagnosis of diabetes using classification mining techniques," in press, 2015.
[5] J. Haddadnia, J. Vahidi, A. Gharakhani, and A. Fouzi, “Fuzzy Diagnosis of Diabetes Mellitus Based on the Comprehension Rules and Characteristics Based on Combination of Data Mining Systems and Intelligent Intelligent Algorithms,” International Conference on Non-linear Modeling and Optimization, 2011.
[6] S. Elsappagh, M. Elmogy, and AM. Riad AM, “A fuzzy ontology oriented case-based reasoning framework for semantic diabetes diagnosis,” Artif Intell Med, vol.14, pp. 92-5, 2015.
[7] J. Buckley, W. Siler, D. J. F. s. Tucker, and systems, “A fuzzy expert system,” vol. 20, no. 1, pp. 1-16, 1986.
[8] Garcia, M. A. et al., “ESDIABETES (AN EXPERT SYSTEM IN DIABETES),” CCSC: South Central Conference, by the Consortium for Computing in Small Colleges, pp. 166–175, 2001.
[9] Ganesh Kumar, P. et al., “Design of a fuzzy expert system for microarray data classification using a novel genetic swarm algorithm,” Expert Systems with Applications. No. 39, pp. 1811–1821, 2012.
[10] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, "Harris hawks optimization: Algorithm and applications," Future Generation Computer Systems, vol. 97, pp. 849-872, 2019.
[11] J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. Johannes, “ Using the ADAP learning algorithm to forecast the onset of diabetes mellitus,” In Proceedings of the 12th symposium on computer applications and medical care, Los Angeles, CA, USA, pp. 261–265, 1998.
[12] K. Polat, S. Günes, and A. Arslan, “A cascade learning system for classification of diabetes disease: Generalized discriminate analysis and least square support vector machine,” Expert Systems with Applications, vol. 34, pp. 482–487, 2008.
[13] K. Saxena, Z. Khan, and S. Singh, "Diagnosis of diabetes mellitus using k nearest neighbor algorithm," vol. 2, pp. 36-43, 2014.
[14] C. S. Lee, “A Fuzzy Expert System for Diabetes Decision Support Application,” IEEE transactions on systems, man, and cybernetics, vol. 41, no. 1, 2011.
[15] M. Kalpana and A.V. Senthilkumar, “Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism,” Internationl Journal of Advanced Networking and Applications, vol. 3, pp. 1128–1134, 2011.
[16] V. Jain and S. Raheja, “Improving the Prediction Rate of Diabetes using Fuzzy Expert System,” I.J. Information Technology and Computer Science, vol. 10, pp. 84-91, 2015.
[17] M. Shirali et al., "Improvement diagnosis of diabetes using a combination of sugeno fuzzy inference systems and firefly algorithms," Iranian Journal of Diabetes and Metabolism, vol. 15, no. 3, pp. 172-176, 2016.
[18] I. Zabbah, A. Eskandari, Z. Sardari, and A. Noghandi, “ Diagnosis of Diabetes using Artificial Neural Network and Neuro-Fuzzy approach,” Journal of Torbat Heydariyeh University of Medical Sciences, vol. 6, no. 2, 2018.
[19] I. Abedian, A. Ayoobi, H. Ghaffary, and I. Zabbah, “ Diagnosis of diabetes by using a data mining method based on native data,” Journal of Torbat Heydariyeh University of Medical Sciences, vol. 7, no. 1, pp. 1-14, 2019.
[20] A. Panah and S. Fallahpour, “Predicting Type2 Diabetes Using Data Mining Algorithms,” J Mazandaran Univ Med Sci, vol. 30, no. 191, pp. 22-30, 2020.
[21] M. Mojarad, E. Hajizadegan, and M. Gurkani, "Diagnosis of Diabetes Using Bee Colony Algorithm and Fuzzy Decision Tree," Int. J. Sci. Res. in Network, vol. 9, pp. 3, 2021.
[22] K. M. Aamir, L. Sarfraz, M. Ramzan, M. Bilal, J. Shafi, and M. Attique, “A Fuzzy Rule-Based System for Classification of Diabetes,” Sensors, vol. 21, no. 23, p. 8095, Dec. 2021.
[23] N. Gundluru et al., "Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model," Computational Intelligence and Neuroscience, vol. 2022, 2022.
[24] P. Nagaraj and P. Deepalakshmi, "An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis," International Journal of Imaging Systems and Technology, 2022.
[25] L. A. ZADEH, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.
[26] W. Siler, J. J. Buckley, “Fuzzy expert systems and fuzzy reasoning,” John Wiley & Sons, 2005.
[27] J. A. Chirinos, A. Veerani, J. P. Zambrano, A. Schob, G. Perez, A. J., Mendez, and S. Chakko, “Evaluation of comorbidity scores to predict all-cause mortality in patients with established coronary artery disease,” International journal of cardiology, vol. 117, no.1, pp. 97-102, 2007.
[28] X.S. Yang, “Nature-inspired metaheuristic algorithms,” Luniver press, 2010.
[29] C. S. Lee and M. H. Wang, “Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition,” Expert System Applications, vol. 33, no. 3, pp. 606–619, 2007.
[30] M. Fasanghari and G. A. Montazer, “Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation,” Expert Systems with Applications, vol. 37, pp. 6138-6147, 2010.
[31] M. Kalpana, A.V. Senthilkumar, “Fuzzy Expert System for Diagnosis of Diabetes Using Fuzzy Determination Mechanism,” International Journal of Computer Science & Emerging Technologies, vol., no. 6, 354-361, 2011.
[32] I. Saritas, A. Ozkan, N. Allahverdi, and M. Argindogan, “Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation,” Springer Science+ Business Media J Intell Manuf, vol. 20, pp. 169-176, 2009.
[33] D.E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, Reading, MA, 1989.
[34] J. Kennedy, R.C. Eberhart, “Particle swarm optimization,” InProc. 1995 IEEE international conference on neural networks, pp. 1942–1948, Australia, 1995.
[35] M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations," International journal of data mining & knowledge management process, vol. 5, no. 2, p. 1, 2015.
[36] O. Oladimeji, “Detecting Breast Cancer through Blood Analysis Data using Classification Algorithms,” Journal of Artificial Intelligence & Data Mining (JAIDM), vol. 9, no. 3, pp. 351-359, 2021.