Automated biomedical measurements analysis: Innovative models based on machine learning for predicting laboratory results in nephrology

Current laboratory prediction systems in nephrology face challenges such as handling non-stationary datasets, limited accuracy, and insufficient personalization. To address these issues, this study introduces three machine learning-based models: the Adaptive Predictive Model for Laboratory Results with Patient-specific Adaptation (APMLR), the Adaptive Input-Output Model for eGFR Prediction based on Other Results (AIOM), and the Intelligent Assessment Model for Renal Function (IAMRF). These models leverage advanced algorithms to improve the accuracy and reliability of predictions for critical parameters such as eGFR, creatinine, and urea levels. The APMLR system achieved superior performance with Linear SVR, reaching a prediction accuracy of up to 96.97%, while Gradient Boosting emerged as the most effective method for both AIOM and IAMRF systems (approx. 95%). These findings highlight the potential of machine learning to enhance nephrology patient care by automating diagnoses, improving operational workflows, and setting a new standard for renal function assessment in clinical practice.