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Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms

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Abstract

Recently machine learning algorithms are widely used for the prediction of different attributes, and these algorithms find widespread applications in a variety of domains. Machine learning in health care has been one of the core areas of research where machine learning models are used on the medical datasets to predict different attributes. This work provides a comparative evaluation of different classical as well as ensemble machine learning models, which are used to predict the risk of diabetes from two different datasets, i.e., PIMA Indian diabetes dataset and early-stage diabetes risk prediction dataset. From the comparative analysis, it is found that the superlearner model provides the best accuracy i.e. 86% for PIMA Indian diabetes dataset, and it provides 97% accuracy for diabetes risk prediction dataset.

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Authors and Affiliations

  1. Department of BCA, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

    Surabhi Saxena

  2. Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Debashish Mohapatra

  3. Department of Electrical and Electronics Engineering, Sambalpur University Institute of Information Technology, Burla, Odisha, India

    Subhransu Padhee

  4. Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Goutam Kumar Sahoo

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  1. Surabhi Saxena
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  2. Debashish Mohapatra
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  3. Subhransu Padhee
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  4. Goutam Kumar Sahoo
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Correspondence to Subhransu Padhee.

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Saxena, S., Mohapatra, D., Padhee, S. et al. Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms. Evol. Intel. 16, 587–603 (2023). https://doi.org/10.1007/s12065-021-00685-9

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  • DOI: https://doi.org/10.1007/s12065-021-00685-9

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