Early-stage kidney disease detection is a critical task in healthcare, and machine learning models provide a promising approach for improving diagnostic accuracy. This study investigates various machine learning algorithms, including Decision Tree, Random Forest, and Gradient Boosting, for predicting chronic kidney disease (CKD) based on publicly available datasets. After preprocessing and feature selection, the models were trained and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The Gradient Boosting model demonstrated the highest accuracy of 85.1%, outperforming other models in distinguishing between patients with and without kidney disease. The results highlight the potential of machine learning as an effective tool for early detection, offering valuable support to healthcare professionals for timely interventions. The findings also emphasize the importance of evaluating multiple metrics to ensure a balanced and reliable diagnosis. This study contributes to the growing body of knowledge on using machine learning for medical predictions and calls for further research to enhance model performance and generalizability.

Kidney disease prediction, machine learning, early detection

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