Simple averaging of Random Forest, Gradient Boosting, and Linear Regression predicted urinary Na/K ratio best (MAE=1.756, R2=0.390) outperforming attention-based models in N=82.
Does a simple ensemble of classical machine learning models improve the estimation of urinary Na/K ratio from basic physiological variables compared to complex deep learning models in a small dataset?
For small datasets, simple ensemble machine learning models provide superior generalization for predicting urinary Na/K ratio compared to complex deep learning architectures.
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The urinary sodium-to-potassium (Na/K) ratio is a clinically established predictor of blood pressure and cardiovascular risk. This study aimed to develop and rigorously evaluate machine learning models for estimating the urinary Na/K ratio using four easily obtainable physiological variables: body weight, systolic blood pressure, diastolic blood pressure, and pulse rate. A dataset of 82 participants was analyzed under a nested cross-validation framework to ensure strict generalization assessment. We first designed an attention-based deep learning model (MIDIP: Multi-Integrated Deep Ion Prediction). Although MIDIP showed reduced training error, nested validation revealed performance instability, indicating overfitting in this small-sample setting. We then compared classical machine learning models and ensemble strategies. Among all configurations, simple averaging of Random Forest, Gradient Boosting, and Linear Regression (Group A) achieved the best performance (MAE = 1.756, RMSE = 2.349, R2 = 0.390). In contrast, incorporating a Transformer model (Group B) degraded performance (MAE = 1.855, R2 = 0.294). Similarly, adaptive weighting (AWE) did not improve accuracy (Group A: MAE = 1.836, R2 = 0.266; Group B: MAE = 2.133, R2 = 0.035). These results demonstrate that, under limited sample conditions (N = 82), model simplicity and equal-weight ensemble integration provide superior generalization compared to attention-based or adaptively weighted deep architectures. The findings underscore the importance of strict validation and controlled model complexity when developing clinically applicable prediction models from small datasets.
Yuda et al. (Sat,) reported a other. Simple averaging of Random Forest, Gradient Boosting, and Linear Regression predicted urinary Na/K ratio best (MAE=1.756, R2=0.390) outperforming attention-based models in N=82.