DEVELOPMENT OF A NONPARAMETRIC REGRESSION FRAMEWORK WITH APPLICATIONS IN BIOSTATISTICS
Keywords:
nonparametric regression, generalized additive models (GAM) multi-layer feedforward neural networks (MLFNN), biostatistics, alanine transferase (ALT), non-normal dataDOI:
https://doi.org/10.17654/0973514325008Abstract
This study develops a methodology using R software for modelling alanine transferase (ALT) with total cholesterol (TC), triglycerides (TG), and alkaline phosphatase (ALP) as predictors. Combining generalized additive models (GAMs) and neural networks (NN), the framework identified ALP as the most significant contributor (80.28%), followed by TG (12.37%) and TC (7.34%). Performance metrics, including RMSE and MAE, demonstrated the neural network’s effectiveness in capturing non-linear relationships, achieving 98.59% accuracy. This practical approach offers valuable insights for biostatistical applications in health and clinical studies.
Received: January 9, 2025
Accepted: January 30, 2025
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