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.

nonparametric regression, generalized additive models (GAM) multi-layer feedforward neural networks (MLFNN), biostatistics, alanine transferase (ALT), non-normal data

Received: January 9, 2025; Accepted: January 30, 2025; Published: February 17, 2025

How to cite this article: Wan Muhamad Amir W Ahmad, Seng Fei Hong, Faiza Awais, Mohamad Nasarudin Adnan, Farah Muna Mohamad Ghazali and Nor Azlida Aleng, Development of a nonparametric regression framework with applications in biostatistics, JP Journal of Biostatistics 25(1) (2025), 177-181. https://doi.org/10.17654/0973514325008

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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