Chronic Kidney Disease (CKD) is a progressive and often asymptomatic condition that poses a significant global health burden, affecting over 850 million individuals worldwide. Early detection is critical to prevent irreversible renal damage; however, traditional diagnostic approaches are time-consuming and prone to human error. This study proposes a lightweight and computationally efficient machine learning framework for the early prediction of CKD using structured clinical data.The proposed methodology utilizes the UCI CKD dataset comprising 400 patient records with 24 attributes. A robust preprocessing pipeline is implemented, including missing value imputation, Z-score-based outlier detection, feature scaling, and correlation- driven feature selection to enhance data quality and reduce dimensionality. Three supervised learning algorithms—Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF)—are evaluated. To ensure model reliability and avoid overfitting, both train-test split (70:30) and 10-fold cross-validation techniques are employed. Experimental results demonstrate that the SGD classifier outperforms other models, achieving an accuracy of 98.33%, precision of 100%, recall of 97.3%, and F1-score of 98.63%. The absence of false-positive predictions highlights its clinical reliability. The proposed framework offers a scalable and real-time decision-support solution, particularly suitable for resource-constrained healthcare environments. Future work will focus on integrating multimodal data, including medical imaging, to further enhance diagnostic accuracy.

This study presents a robust and efficient machine learning framework for the early prediction of Chronic Kidney Disease using structured clinical data. The integration of preprocessing techniques, correlation-based feature selection, and optimized machine learning models enables high predictive accuracy while maintaining computational efficiency. Among the evaluated models, the SGD classifier demonstrated superior performance, achieving high accuracy and zero false positives, making it highly suitable for clinical decision support systems. The proposed approach offers a scalable solution for real-time CKD prediction, particularly in resource- constrained healthcare environments. Future research will focus on incorporating multimodal data and enhancing model interpretability to facilitate broader clinical adoption.

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