Machine Learning Identification of Oxidative Stress Biomarkers for Early Chronic Kidney Disease Detection
Keywords:
Oxidative stress, chronic kidney disease, machine learning, hypertension, biomarkers, predictive modelingAbstract
Oxidative stress plays a pivotal role in chronic kidney disease (CKD) pathogenesis, particularly in hypertensive patients, creating a self-perpetuating cycle of renal damage. This study investigated oxidative stress mechanisms in chronic hypertension and renal impairment while validating related clinical biomarkers through machine learning approaches. A comprehensive analysis was conducted using clinical datasets containing 25 parameters from patients with varying kidney function stages. Three machine learning algorithms i.e. K-Nearest Neighbors, Decision Tree, and AdaBoostwere employed for CKD prediction, with performance evaluated using accuracy, precision, recall, and AUC metrics. The Decision Tree algorithm achieved exceptional performance with 97.5% accuracy and 0.969 AUC, followed by AdaBoost (96.7% accuracy, 0.962 AUC), while KNN showed moderate performance (66.7% accuracy). Significant distributional differences between CKD and non-CKD populations were observed for oxidative stress-related parameters, with blood glucose showing pronounced separation consistent with hyperglycemia-induced oxidative stress. Hemoglobin and albumin levels reflected oxidative damage-associated anemia and compromised antioxidant defenses, respectively. The superior performance of tree-based algorithms suggests discrete oxidative stress thresholds aligning with established pathophysiological mechanisms. These findings demonstrate that machine learning can effectively validate oxidative stress-related biomarkers for CKD prediction, supporting computational approaches for early detection and personalized antioxidant intervention strategies to break the oxidative stress cycle in kidney disease progression
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