Citation

Sharifuzzaman, Md and Fahad, Nafiz and Rabbi, Riadul Islam and Sadib, Ridwan Jamal and Haque Tusher, Ekramul and Liew, Tze Hui and Naimul Islam, Mohammad and Ullah Miah, Md Saef and Hossen, Md. Jakir (2025) Optimized Machine Learning Models and Stacking Hybrid Approach for Chronic Kidney Disease Prediction. In: 21st IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2025, 26 -29 June 2025, Limassol, Cyprus.

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Abstract

Chronic Kidney Disease (CKD) is a severe disease that requires being diagnosed at an early stage to stop potential complications. Machine learning methods show an effective solution for predicting CKD, which enables healthcare professionals to make timely decisions. However, the selection of proper models along with their performance optimization remains the key essential challenge. This study examines the effectiveness of several machine learning algorithms, such as k-nearest neighbours (KNN), decision tree classifier (DT), logistic regression, naïve bayes (NB), and gradient boosting (GB), in predicting CKD cases. In addition, a stacking hybrid model focuses on enhancing classification effectiveness as well as model robustness for preventing overfitting. Each baseline model undergoes hyperparameter tuning for maximizing accuracy through optimal setting determination. Performance evaluation of the models uses multiple measurement criteria that include accuracy, precision, recall, f1-score, Jaccard score, and AUC-ROC curve, where the optimized model and hybrid model have shown better results. These metrics provide a complete insight into how well models classify CKD and non-CKD cases correctly. The results demonstrate that each optimized model improves model performance by using hyperparameter tuning. Moreover, the stacking hybrid model (using 3 of the best optimized models) performs better than each of the optimized classifiers, showing that the potential of integrating various models can boost the ability of the prediction. This study highlights the optimized model and a well-tuned hybrid model can be a valuable approach for enhancing diagnostic accuracy in medical decision-making, aiding in the early identification and management of CKD. However, The source code of this study is available at this link: https://github.com/dragontea72/Chronic-Kidney-Disease-Prediction-.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Chronic kidney disease, machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Engineering (FOE) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Jul 2025 04:53
Last Modified:	31 Jul 2025 21:31
URII:	http://shdl.mmu.edu.my/id/eprint/14375

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