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HFBO-KSELM: Hybrid Flash Butterfly Optimization-based Kernel Softplus Extreme Learning Machine for Classification of Chronic Kidney Disease

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Abstract

The progressive damage of kidney function and structure exhibits a high risk of cardiovascular disease and is the leading cause of life-threatening chronic kidney disease (CKD). Early diagnosing is the only way to prevent CKD from worsening. Different data mining and machine learning approaches are implemented for predicting chronic kidney disease; however, the extraction of hidden details from the clinical data is a difficult challenge. To overcome this situation, a new model for accurate prediction of chronic kidney disease is proposed in this article. This work evaluated the proposed predictive model using a CKD dataset. The raw chronic kidney disease dataset is initially preprocessed using data cleaning, data reduction as well as data transformation steps. These steps make the classification system easier to predict diseased and normal classes by enhancing the data quality. The features are then selected using a hybrid flash butterfly optimization algorithm. Here, the weight functions of the kernel soft plus extreme learning machine (KSELM) method are optimally tuned using the hybrid flash butterfly optimization (HFBO) algorithm to increase prediction accuracy. The efficiency of the proposed HFBO-KSELM algorithm is examined using different evaluation measures namely precision, accuracy, and specificity, recall, F1-score, and computation time. The results of the experimental analysis display superior performance of the proposed HFBO-KSELM algorithm over other existing methods, particularly with an accuracy of 97.8%, precision of 97.4%, recall of 96.9%, specificity of 97.5%, F1-score of 97.1%, computational time of 2.3 s, training accuracy of 0.978, testing accuracy of 0.94 and ROC/AUC of 0.97. Finally, the KSELM algorithm accurately predicts and classifies them into benign and malignant classes. The experimental result showed that the proposed HFBO-KSELM algorithm provides the effectiveness and robustness to detect chronic kidney disease.

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Authors and Affiliations

  1. Cloud Computing and Wireless Sensor Lab, IIT Roorkee, SRE, Uttar Pradesh, India

    Pooja Yadav & S. C. Sharma

  2. Department of Computer Science and Information Technology, MJP Rohilkhand University, Bareilly, Uttar Pradesh, India

    Pooja Yadav

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  1. Pooja Yadav
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  2. S. C. Sharma
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Contributions

All authors agreed on the content of the study. PY and SCS collected all the data for analysis. PY agreed on the methodology. PY and SCS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to Pooja Yadav.

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Yadav, P., Sharma, S.C. HFBO-KSELM: Hybrid Flash Butterfly Optimization-based Kernel Softplus Extreme Learning Machine for Classification of Chronic Kidney Disease. J Supercomput 79, 17146–17169 (2023). https://doi.org/10.1007/s11227-023-05337-6

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  • DOI: https://doi.org/10.1007/s11227-023-05337-6

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