• Logo
  • HamaraJournals

Improving Diagnosis Accuracy of Diabetic Disease Using Radial Basis Function Network and Fuzzy Clustering

Hadiseh Hosseini, Amid Khatibi Bardsiri



Introduction: Nowadays, medical sciences and physicians face a huge amount of data. Diabetes is one of the most expensive glands in the world. Since it is not always easy to diagnose the disease, the physician should examine the outcome of patient tests and decisions made in the past for patients with similar conditions to make an appropriate decision. Due to the large number of patients and the multiple tests performed on each patient, an automated tool for exploring previous patients is needed.

Materials and Methods: One of the most important methods used to derive data is data mining. Due to the high number of diabetic patients, timely diagnosis and treatment of this disease can reduce the risk of death and its associated medical costs. So far, different systems have been proposed for the diagnosis and prediction of diabetes, but fuzzy logic based systems are used in this study to increase accuracy and efficiency. In the proposed model, fuzzy clustering is first grouped into separate clusters, and then the radial neural network is predicted for each patient with diabetes mellitus. A compatible neuro-fuzzy inference system has also been used to diagnose diabetes.

Results: In this paper different classification techniques have been used in MATLAB software to diagnose diabetes mellitus and to classify patients as diabetic and non diabetic. The dataset used is extracted from the UCI database. The accuracy of the proposed method is 97.14% which is significantly higher than other models of diabetes diagnosis.

Conclusion: The application of two fuzzy models has significantly improved the accuracy of diagnosis of diabetes compared to other models proposed in this field.


Nazarzadeh M, Bidel Z, Sanjari Moghaddam A. Meta analysis of diabetes mellitus and risk of hip fractures: Small study effect. Osteoporos Int. 2016; 27(1): 229-30. PMID: 26501557 DOI: 10.1007/s00198-015-3358-9

Janahmadi Z, Nekooeian AA, Mozafari M. Hydroalcoholic extract of Allium eriophyllum leaves attenuates cardiac impairment in rats with simultaneous type 2 diabetes and renal hypertension. Res Pharm Sci. 2015; 10(2): 125-33. PMID: 26487889

Al Jarullah AA. Decision tree discovery for the diagnosis of type II diabetes. International Conference on Innovations in Information Technology (IIT). IEEE; 2011.

Khajehei M, Etemady F. Data mining and medical research studies. International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE; 2010.

Jayalakshmi T, Santhakumaran A. A novel classification method for diagnosis of diabetes mellitus using artificial neural networks. International Conference on Data Storage and Data Engineering (DSDE). IEEE; 2010.

Zadeh LA. Fuzzy sets. Information and Control. 1965; 8(3): 338-53.

Ruspini ER. A new approach to clustering. Information and Control. 1969; 15(1): 22-32.

Dunn JC. A fuzzy relative of the ISODATA process and its uses in detecting compact well-seperated clusters. Journal of Cybernetics and Systems. 1973; 3(3): 32-57.

Chiu SL. A cluster estimation method with extension to fuzzy model identification. Conference on Fuzzy Systems. IEEE; 1994.

Hathaway RJ, Bezdek JC, Hu Y. Generalized fuzzy c-means clustering strategies using LP norm distances. IEEE Trans on Fuzzy Systems. 2000; 8(5): 576-82.

Asrardel M. Prediction of combustion dynamics in an experimental turbulent swirl stabilized combustor with secondary fuel injection. [MSc Thesis] University of Tehran; 2015.

Rezakazemi M, Mosavi A, Shirazian S. ANFIS pattern for molecular membranes separation optimization. Journal of Molecular Liquids. 2019; 274: 470-6.

Tan PN, Steinbach M, Karpatne A, Kumar V. Introduction to data mining. 2nd Ed. Pearson Publication: India; 2018.

Berka P, Rauch J, Zighed DA. Data mining and medical knowledge management: Cases and applications. Hershey: Idea Group Inc (IGI); 2009.

Su CT, Yang CH, Hsu KH, Chiu WK. Data mining for the diagnosis of type II diabetes from three dimensional body surface anthropometrical scanning data. Computers and Mathematics with Applications. 2006; 51(6-7): 1075-92.

Purnami SW, Embong A, Zain JM, Rahayu SP. A new smooth support vector machine and its applications in diabetes disease diagnosis. Journal of Computer Science. 2009; 5(12): 1003-8.

Barakat NH, Bradley AP, Barakat MN. Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed. 2010; 14(4): 1114-20. PMID: 20071261 DOI: 10.1109/TITB.2009.2039485

Patil BM, Joshi RC, Toshniwal D. Association rule for classification of type -2 diabetic patients. Second International Conference on Machine Learning and Computing. IEEE; 2010.

Richards G, Rayward-Smith VJ, Sönksen PH, Carey S, Weng C. Data mining for indicators of early mortality in database of clinical records. Artif Intell Med. 2001; 22(3): 215-31. PMID: 11377148 DOI: 10.1016/s0933-3657(00)00110-x

Breault JL, Goodall CR, Fos PJ. Data mining a diabetic data warehouse. Artif Intell Med. 2002; 26(1-2): 37-54. PMID: 12234716 DOI: 10.1016/s0933-3657(02)00051-9

Sigurdardottir AK, Jonsdottir H, Benediktsson R. Outcomes of educational interventions in type 2 diabetes: WEKA data-mining analysis. Patient Educ Couns. 2007; 67(1-2): 21-31. PMID: 17420109 DOI: 10.1016/j.pec.2007.03.007

Huang Y, McCullagh P, Black N, Harper R. Feature selection and classification model construction on type 2 diabetic patients’ data. Artif Intell Med. 2007; 41(3): 251-62. PMID: 17707617 DOI: 10.1016/j.artmed.2007.07.002

DOI: http://dx.doi.org/10.30699/fhi.v8i1.203


  • There are currently no refbacks.