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An Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational Intelligence

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Information and Communication Technology and Applications (ICTA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1350))

Abstract

Artificial Intelligence (AI) in medicine has provided numerous advantages in diagnosis, management, and prediction of highly complicated and uncertain diseases like diabetes. Despite the high rate of complexity and uncertainty in this area, computational intelligent systems such as the Artificial Neural Network (ANN), Fuzzy Logic (FL) and Genetic Algorithm (GA) have been used to enhance healthcare services, reduce medical costs and improve quality of life. Hence, Computational Intelligence Techniques (CIT) has been successfully employed in diabetes disease diagnosis, risk evaluation, patient monitoring, and prediction in the medical field. Using single technique in the diagnosis of diabetes has been comprehensively investigated showing some level of accuracy, but the use of hybridized can still perform better. Diabetes Mellitus (DM) is one of contemporary society’s most chronic and crippling diseases and poses not just a medical issue but also a socio-economic issue. Therefore, the paper develops an improved hybrid system for the diagnosis of diabetes mellitus using FL, ANN, and Genetic Algorithm (GA). FL and ANN was combined for the diagnosis of diabetes mellitus and GA is used for features selection and optimization. The result performed better during the diagnosis process for diabetes mellitus. Hence the results of the comparison showed that Genetic-Neuro-Fuzzy Inferential System (GNFIS) had a better performance with 99.34% accuracy on the whole dataset used when compared with FL and ANN with 96.14% and 95.14% respectively. The proposed system can be used in assisting medical practitioners in diagnose diabetes mellitus and increase its accuracy

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Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Idowu Dauda Oladipo, Abdulrauph Olarewaju Babatunde, Joseph Bamidele Awotunde & Muyideen Abdulraheem

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  1. Idowu Dauda Oladipo
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  2. Abdulrauph Olarewaju Babatunde
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  3. Joseph Bamidele Awotunde
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  4. Muyideen Abdulraheem
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Corresponding author

Correspondence to Joseph Bamidele Awotunde .

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Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. Federal University of Technology Minna, Minna, Nigeria

    Bilkisu Muhammad-Bello

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Oladipo, I.D., Babatunde, A.O., Awotunde, J.B., Abdulraheem, M. (2021). An Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational Intelligence. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_22

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  • DOI: https://doi.org/10.1007/978-3-030-69143-1_22

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  • Online ISBN: 978-3-030-69143-1

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