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Diabetes Classification Techniques: A Brief State-of-the-Art Literature Review

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Applied Informatics (ICAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1277))

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Abstract

Clinical data on diabetes patients are readily available in many countries and research directories. These data are not necessarily in the same format or do not contain error-free or clear information about diabetes. These incomprehensive and non-homogenous data are a great source of conflict for the practitioner and the research communities. Applying Computational Intelligence on these datasets makes it easier for patterns and relationships to be identified, and useful information or conclusions can be derived. Diabetes diagnosis falls under the data classification problem and so much literature exists in this subject area. This study aims to survey as much literature as can be found on the application of computational intelligence for diabetes classification from 2010–2020. Articles indexed in Scopus, IEEE, Web of Science, Google Scholar, and other scholarly databases were searched for up-to-date articles and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used for article selection. We selected articles after inclusion and exclusion criteria were applied. We discuss the commonly used diabetes classification algorithms and datasets. Finally, a taxonomy based on whether the algorithms are standalone or hybrid and whether they are a variant of major algorithms or a comparative study is presented.

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  1. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa

    Jeffrey O. Agushaka & Absalom E. Ezugwu

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  1. Jeffrey O. Agushaka
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Correspondence to Absalom E. Ezugwu .

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    Hector Florez

  2. Covenant University, Ota, Nigeria

    Sanjay Misra

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Agushaka, J.O., Ezugwu, A.E. (2020). Diabetes Classification Techniques: A Brief State-of-the-Art Literature Review. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_22

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