| 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. |
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