JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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DEVELOPMENT OF A NONPARAMETRIC REGRESSION FRAMEWORK WITH APPLICATIONS IN BIOSTATISTICS

Authors

  • Wan Muhamad Amir W Ahmad
  • Seng Fei Hong
  • Faiza Awais
  • Mohamad Nasarudin Adnan
  • Farah Muna Mohamad Ghazali
  • Nor Azlida Aleng

Keywords:

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

DOI:

https://doi.org/10.17654/0973514325008

Abstract

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

References

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Published

2025-02-17

Issue

Section

Articles

How to Cite

DEVELOPMENT OF A NONPARAMETRIC REGRESSION FRAMEWORK WITH APPLICATIONS IN BIOSTATISTICS. (2025). JP Journal of Biostatistics, 25(1), 177-181. https://doi.org/10.17654/0973514325008

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