Parameter Tuning Using Harris Hawks Optimization for Improved Chronic Kidney Disease Classification

Section: Research Article
Issue
Vol. 35 No. 2 (2026): Volume 35, Issue 2
Pages
31-39

Keywords:

Machine learning , Swarm Intelligence, Hyperparameter Tuning Imbalanced Dataset, Harris Hawks Optimization

Abstract

At an early phase, chronic kidney disease (CKD) is usually not obvious. An appreciable reduction in kidney function is the primary sign of the disease. If CKD can be identified early, the rate at which CKD is advancing can be slowed down, and complications can be avoided. This study proposes using a swarm intelligence model for hyperparameter tuning. Hyperparameter tuning of SVM, RF, and GB classifiers was performed using the HHO Algorithm. The CKD dataset imbalance has been addressed by using SMOTE. Results of the study demonstrate that the HHO hyperparameter tuning methodology offered the best performance with respect to accuracy, F1-score, and ROC AUC. These results show that the HHO hyperparameter tuning methodology yields an increase in classification performance.

References

  1. P. Aravazhi, P. Gunasekaran, N. Z. Y. Benjamin, A. Thai, K. K. Chandrasekar, N. Kolanu, P. Prajjwal, Y. Tekuru, L. Brito, &P. Inban, “The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions,” Disease-a-Month, vol. 71, 2025, doi: 10.1016/j.disamonth.2025.101882.
  2. S. Maringhini and C. Zoccali, “Chronic kidney disease progression—A challenge,” Biomedicines, vol. 12, no. 10, p. 2203, 2024, doi: 10.3390/biomedicines12102203.
  3. P. Ventrella, G. Delgrossi, G. Ferrario, M. Righetti, &M. Masseroli, “Supervised machine learning for the assessment of chronic kidney disease advancement,” Computer Methods and Programs in Biomedicine, vol. 209, p. 106329, 2021, doi: 10.1016/j.cmpb.2021.106329.
  4. F. Hosseini, “Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain,” Journal of Hydrology, vol. 643, 2024.
  5. B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, T. Ullmann, M. Becker, A.-L. Boulesteix, D. Deng, &M. Lindauer, “Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 13, 2023, doi: 10.1002/widm.1484.
  6. K. Rajwar, K. Deep, &S. Das, “An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges,” Artif. Intell. Rev., vol. 56, pp. 13187–13257, 2023, doi: 10.1007/s10462-023-10470-y.
  7. G. Hussien, L. Abualigah, R. Zitar, F. Hashim, M. Amin, A. Saber, K. Almotairi, & A. Gandomi, “Recent advances in Harris Hawks optimization: A comparative study and applications,” Electronics, vol. 11, 2022, doi: 10.3390/electronics11121919.
  8. W. Yang, N. Ahmed, &A. Barczak, “Comparative analysis of machine learning algorithms for CKD risk prediction,” IEEE Access, pp. 1–1, 2024, doi: 10.1109/ACCESS.2024.3499355.
  9. B. Metherall, A. Berryman, & G. Brennan, “Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements,” Sci. Rep., vol. 15, 2025, doi: 10.1038/s41598-025-88631-y.
  10. H. Ilyas, S. Ali, M. Ponum, O. Hasan, M. Mahmood, M. Iftikhar, & M. Malik, “Chronic kidney disease diagnosis using decision tree algorithms,” BMC Nephrol., vol. 22, 2021, doi: 10.1186/s12882-021-02474-z.
  11. N. Khan, M. Raza, N. Mirjat, N. Balouch, G. Abbas, A. Yousef, & E. Touti, “Unveiling the predictive power: A comprehensive study of machine learning model for anticipating chronic kidney disease,” Frontiers in Artificial Intelligence, vol. 6, 2024, doi: 10.3389/frai.2023.1339988.
  12. S. Prakash, P. Raja, A. Baseera, D. Hussain, V. Balaji, & K. Venkatachalam, “Ensemble nonlinear support vector machine approach for predicting chronic kidney diseases,” Computer Systems Science and Engineering, vol. 42, pp. 1273–1287, 2022, doi: 10.32604/csse.2022.021784.
  13. L. Kethineni, Nithinchandra, N. Kumar, & S. Sk, “Chronic kidney disease prediction based on machine learning algorithms,” ITM Web of Conferences, vol. 74, 2025, doi: 10.1051/itmconf/20257401004.
  14. P. Theerthagiri & A. U. Ruby, “RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction,” Expert Systems, vol. 39, 2022, doi: 10.1111/exsy.13048.
  15. N. Khan, M. Raza, N. Mirjat, N. Balouch, G. Abbas, A. Yousef, & E. Touti, “Unveiling the predictive power: A comprehensive study of machine learning model for anticipating chronic kidney disease,” Frontiers in Artificial Intelligence, vol. 6, 2024, doi: 10.3389/frai.2023.1339988.
  16. M. A. Islam, M. Z. H. Majumder, &M. A. Hussein, “Chronic kidney disease prediction based on machine learning algorithms,” J. Pathol. Inform., vol. 14, p. 100189, Jan. 2023, doi: 10.1016/j.jpi.2023.100189.
  17. H. Gezici & H. Livatyali, “Chaotic Harris hawks optimization algo rithm,” Journal of Computational Design and Engineering, vol. 9, pp. 216–245, 2022, doi: 10.1093/jcde/qwab082.
  18. Z. Wang & X. Wei, “Harris hawk optimization algorithm with combined perturbation strategy and its application,” Scientific Reports, vol. 15, 2025, doi: 10.1038/s41598-025-04705-x.
  19. A. A. Heidari, H. Faris, I. Aljarah, M. Mafarja, & H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems, vol. 97, pp. 849–872, 2019, doi: 10.1016/j.future.2019.02.028.
  20. M. Dimgba &R. Andreas, “Optimal hyperparameter search strategies: Benchmarking grid search, random search, and genetic algorithms across regression, classification, and clustering tasks,” ResearchGate, 2024, doi: 10.13140/RG.2.2.24957.37603.

Authors

Omar Shakir Hasan

Maan Y Anad Alsaleem

Directorate of Educational Nineveh Mosul, Iraq

Identifiers

DOI: 10.33899/jes.v35i2.53649

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[1]
“Parameter Tuning Using Harris Hawks Optimization for Improved Chronic Kidney Disease Classification”, JES, vol. 35, no. 2, pp. 31–39, Apr. 2026, doi: 10.33899/jes.v35i2.53649.
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How to Cite

[1]
“Parameter Tuning Using Harris Hawks Optimization for Improved Chronic Kidney Disease Classification”, JES, vol. 35, no. 2, pp. 31–39, Apr. 2026, doi: 10.33899/jes.v35i2.53649.