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Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model

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Abstract

The World Health Organization (WHO) estimated that in 2016, 1.6 million deaths caused were due to diabetes. Precise and on-time diagnosis of type-II diabetes is crucial to reduce the risk of various diseases such as heart disease, stroke, kidney disease, diabetic retinopathy, diabetic neuropathy, and macrovascular problems. The non-invasive methods like machine learning are reliable and efficient in classifying the people subjected to type-II diabetics risk and healthy people into two different categories. This present study aims to develop a stacking-based integrated kernel extreme learning machine (KELM) model for identifying the risk of type-II diabetic patients based on the follow-up time on the diabetes research center dataset. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. A min-max normalization is used to preprocess the noisy datasets. The Hybrid Particle Swarm Optimization-Artificial Fish Swarm Optimization (HAFPSO) algorithm used satisfies the multi-objective problem by increasing the Classification Accuracy (CA) and decreasing the kernel complexity of the optimal learners (NBC) selected. At last, the model is integrated by utilizing the KELM as a meta-classifier which combines the predictions of the twenty Base Learners as a whole. The proposed classification method helps the clinicians to predict the patients who are at a high risk of type-II diabetes in the future with the highest accuracy of 98.5%. The proposed method is tested with different measures such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics are calculated. The results obtained show that the KELM-HAFPSO approach is a promising new tool for identifying type-II diabetes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GKM College of Engineering and Technology, Chennai, India

    N. Kanimozhi

  2. Department of Information Technology, K S Rangasamy College of Engineering, Tiruchengode, India

    G. Singaravel

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Kanimozhi, N., Singaravel, G. Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model. Med Biol Eng Comput 59, 841–867 (2021). https://doi.org/10.1007/s11517-021-02333-x

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