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OptiDiab: revolutionizing diabetes detection with the binary bald eagle search algorithm

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Abstract

With the continuous rise in the count of deadly diseases that risk either human life or health, the medical Decision Support System keeps proving its efficiency in providing healthcare professionals and other physicians with support in making clinical decisions. Diabetes mellitus is assumed that chronic disease where the body doesn’t produce the essential amount of insulin or insulin is not utilized well by the body, which leads to extremely higher glucose (blood sugar) levels. At the same time, once diabetes has been left untreated or undetected, it may cause severe harm to the body and makes it challenging to treat, but earlier diabetes diagnosis may result in better treatment, giving rise to lower death and morbidity. The Binary Bald Eagle Search Algorithm with Optimal Fuzzy Rule-based Classifier algorithm is meticulously designed to achieve highly effective diabetes detection and classification. It incorporates a Binary Bald Eagle Search Algorithm for optimal feature subset selection and employs the Fuzzy Rule-based Classifier for diabetes detection. To further enhance its performance, the algorithm utilizes sand cat swarm optimization to optimize the parameter values of the Fuzzy Rule-based Classifier. Extensive experimentation on benchmark diabetes medical datasets demonstrates the superiority of the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier approach over state-of-the-art models. The main objective of the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier algorithm is to accomplish effective detection and classification of diabetes. To achieve this, the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier technique primarily designs a Binary Bald Eagle Search Algorithm system for the optimal selection of feature subsets. Additionally, the detection of diabetes takes place using the Fuzzy Rule-based Classifier technique. Furthermore, the sand cat swarm optimization system was applied to optimise the Fuzzy Rule-based Classifier algorithm's parameter values. A wide range of experimental analyses is carried out on benchmark diabetes medical datasets, and the outcome was examined under many aspects. The experimental outcome portrayed the greater of the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier approach over the state of art models.

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Authors and Affiliations

  1. Department of Computer Science, Alagappa University, Karaikudi, India, 630003

    R. Karthikeyan & E. Ramaraj

  2. PG Department of Computer Science, Dr. Umayal Ramanathan College for Women, Karaikudi, India, 630003

    P. Geetha

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  1. R. Karthikeyan
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  2. P. Geetha
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  3. E. Ramaraj
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Correspondence to R. Karthikeyan.

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Karthikeyan, R., Geetha, P. & Ramaraj, E. OptiDiab: revolutionizing diabetes detection with the binary bald eagle search algorithm. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18339-0

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