Soft-computing: An Objective Approach in Varied Diabetes Recognition


  • Obi Jonathan Chukwuyeni UNIVERSITY OF BENIN



Fuzzy Logic, Fuzzy Set, Fuzzy Linguistic variables Genetic Algorithm, Neural Network


Diabetes is a chronic disorder caused by elevated glucose within the blood stream. The predominant indicator of diabetes include a glucose level of more 125mg/dl in addition to frequent thirst, unusual thirst, extreme fatigue blurred vision and frequent infection. Existing approach for the recognition of diabetes are to two classes (Type I and Type II) in addition to their subjective approach. This research paper proposed an objective approach utilizing soft-computing techniques for the recognition of five class of diabetes.


Ahmad H. (2011), “Fuzzy approach to Likert Spectrum in Classified levels in surveying researches” retrieved

Angel C. and Rocio R. ( 2011), “Documentation management with Ant colony Optimization Meta-heuristic: A Fuzzy Text Clustering Approach Using Pheromone trails” retrieved from soft computing in Industrial applications, Advances in intelligent and soft Computing, vol. 96, 2011, 261-70, DOI: 10.1007/978-3-642-20505-1_23

DLF: Diabetes Leadership Forum (2011), “Diabetes: the hidden pandemic and its impact on Sub-Saharan Africa”retrieved online from

Diogo F. P., Flávio R.S. O. and Fernando B. L. N (2008), “Multi-objective abilities in the Hybrid Intelligent Suite for decision support” retrieved from http://ieeexplore.

Gutiérrez P.A. (2011), “Hybrid Artificial Neural Networks: Models”, retrieved online from http://

Kemi O. (2012), “Three Nigerians Suffering from Diabetes” retrieved online from

Kuang Y. H.; Ting-H. C. and Ting-Cheng Chang (2011), “Determination of the threshold value β of variable precision rough set by fuzzy algorithms” retrieved from

Madhavi G. and Bamnote K. (2012), Predictive Diagnosis Model” Clinical Microbiology Reviews, Vol.13, No.1, Pp. 76-82.

Robert F. (2000) “Introduction to Neuro-Fuzzy Systems: Advances in Soft Computing Series”, Springer-Verlag, Berlin/Heidelberg, Germany.

Shanti W. P., Abdullah E., Jasni M. Z, and Rahayu S.P. (2009), “A New Smoth Vector Machine and Its Applications in Diabetes Disease Diagnosis”, Journal of Computer Science Vol. 5(12): 1003-1008

Sun C.T. and Jang J.S. (1993) “A neuro-fuzzy classifier and its applications”, in: Proc. IEEE Int. Conference on Neural Networks, San Francisco, pp.94–98.

Zadeh L.A. (1965), “Fuzzy sets. Information and Control”, Vol.8, pp.338-353.




How to Cite

Chukwuyeni, O. J., & Imianvan, A. A. (2014). Soft-computing: An Objective Approach in Varied Diabetes Recognition. Journal of Biomedical Engineering and Medical Imaging, 1(5), 23–33.