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Fuzzy Logic-Based Predictive Model for the Risk of Type 2 Diabetes Mellitus

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  • Peter Adebayo Idowu

    (Obafemi Awolowo University Nigeria, Ife, Nigeria)

  • Jeremiah Ademola Balogiun

    (Obafemi Awolowo University, Ife, Nigeria)

Abstract

This article presents a predictive model that can be used for the early detection of Type 2 Diabetes Mellitus using fuzzy logic. In order to formulate the model, risk factors associated with the risk of T2DM were elicited. The predictive model was formulated using fuzzy triangular membership functions following which the rules needed for the inference engine was elicited from experts. The model was simulated using the MATLAB Fuzzy logic Toolbox. The results of the study showed that the sensitivity of 11.67% and 100% precision for the low risk was recorded for both cases, specificity of 41.67% compared to 48.33% for the moderate risk, while there was 0% and 13.33% for the high risk. In conclusion, this model will help the doctor to know what course of preventive actions for a patient with high risk and what advice to give to those with low and moderate risk so that the occurrences of the diseases can be prevented altogether and thereby reducing the number of people dying from Type 2 Diabetes Mellitus diseases worldwide.

Suggested Citation

  • Peter Adebayo Idowu & Jeremiah Ademola Balogiun, 2019. "Fuzzy Logic-Based Predictive Model for the Risk of Type 2 Diabetes Mellitus," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 10(3), pages 56-78, July.
    • Handle: RePEc:igg:jehmc0:v:10:y:2019:i:3:p:56-78
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