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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MODELING AND VALIDATING QUALITATIVE PREDICTORS IN MULTIPLE LINEAR REGRESSION USING NEURAL NETWORKS

Authors

  • Nor Azlida Aleng
  • Wan Muhamad Amir W Ahmad
  • Nurfadhlina Abdul Halim
  • Syerrina Zakaria
  • Norsamsu Arni Samsudin
  • Mohamad Nasarudin Adnan
  • Farah Muna Mohamad Ghazali

Keywords:

linear regression, MLFF, qualitative predictor, blood pressure

DOI:

https://doi.org/10.17654/0973514325029

Abstract

This study aims to establish the fundamental concepts of multiple linear regression models involving qualitative predictor variables and to validate their associations using a Multilayer Feed Forward (MLFF) neural network. A simple guide is introduced to separate qualitative variables based on the number of classes, ensuring they meet the assumptions of multiple linear regression. The approach provides a basic and practical template for integrating qualitative predictors into applied linear models. Validation is carried out using the sum of square error and relative error obtained from the MLFF neural network. The low error values produced by the MLFF model highlight the effectiveness and superiority of the proposed methodology.

Received: June 7, 2025
Accepted: July 25, 2025

References

[1] W. M. A. W. Ahmad, F. Ahmed and M. N. Adnan, Coordinating qualitative predictor variables in an applied linear model: analysis and application for applied sciences, Cureus Journal of Medical Science 16 (2024), e59151.

[2] W. M. A. W. Ahmad, M. N. Adnan, M. S. M. Ibrahim, N. A. Samsudin, N. F. M. Noor, N. A. Aleng and S. N. Hassan, Developing a hybrid linear model with a multilayer feed-forward neural network for HbA1c modeling among diabetes patients, Asian Journal of Fundamental and Applied Sciences 4(1) (2023), 41-49.

[3] Q. Zhang, Z. Li, S. Snowling, A. Siam and W. El-Dakhakhni, Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network, Water Science and Technology 80(2) (2019), 243-253.

[4] O. Üstün, E. Bekiroğlu and M. Önder, Design of highly effective multilayer feed forward neural network by using genetic algorithm, Expert Systems 37(4) (2020), 1-19.

[5] F. Li, Y. Lokhnygina, D. M. Murray, P. J. Heagerty and E. R. DeLong, An evaluation of constrained randomization for the design and analysis of group-randomized trials, Stat. Med. 35(10) (2016), 1565-1579.

[6] J. P. Kleijnen, Design and Analysis of Simulation Experiments, Springer International Publishing, 2018, pp. 3-22.

[7] W. M. A. W. Ahmad, M. S. M. Ibrahim, A. R. Hanafi, L. Puspa and N. A. Aleng, Algorithm for combining robust and bootstrap in multiple linear model regression, Journal of Modern Applied Statistical Methods 15(1) (2016), 884-892.

[8] L. Knoll, L. Breuer and M. Bach, Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning, Science of the Total Environment 668 (2019), 1317-1327.

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Published

2025-09-06

Issue

Vol. 25 No. 3 (2025)

Section

Articles

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Copyright (c) 2025 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA

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How to Cite

MODELING AND VALIDATING QUALITATIVE PREDICTORS IN MULTIPLE LINEAR REGRESSION USING NEURAL NETWORKS. (2025). JP Journal of Biostatistics, 25(3), 533-543. https://doi.org/10.17654/0973514325029

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