Gender-specific classification of subclinical liver and renal dysfunction in older adults using machine learning and cytokine profiling.

Background: This study leverages machine learning and cytokine profiles to differentiate liver and renal function abnormalities in the aging population, aiming for advancements in early detection techniques. Methods: The analysis involved data from 760 participants, employing logistic regression, random forest, lasso regression, extreme gradient boosting, and support vector machines to create diagnostic models. Cytokine levels were measured via ELISA, alongside liver and renal clinical function tests. The data were randomly split 3:1 into training and hold-out validation sets; Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively to the training set to mitigate class imbalance. Models were assessed on precision, recall, F1 score, specificity, and the area under the curve (AUC). Results: Lasso regression was notably effective in identifying renal function abnormalities, delivering AUCs of 0.895 for males and 0.940 for females, pointing to its robustness in feature selection and model accuracy. For liver function, logistic regression was most accurate, with AUCs of 0.918 for males and 0.794 for females, identifying VCAM-1, REG4, Thrombomodulin, Notch-3 for males, and GDF-15, LDL R, CA125, PON1 for females as key discriminative cytokines. These results illustrate the models' capability in discerning critical biomarkers for early detection, with performance improved by SMOTE through correction of class imbalance in the training data. Conclusion: Integrating machine learning with cytokine profiling emerges as a highly promising method for early detection of liver and renal abnormalities in the aging population, suggesting significant potential for improving preventive healthcare outcomes.