Research Article

Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147

Volume: 10 Number: 2 July 16, 2026
EN TR

Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147

Abstract

This study aims to develop and validate a machine learning model for accurate and rapid prediction of required brick wall thickness for diagnostic X-ray rooms, based on the methodology of NCRP Report No. 147, and to assess its performance using real-world clinical data from Myanmar. A synthetic dataset of 864,000 samples was generated based on the shielding design methodology in NCRP Report No. 147. A Random Forest regressor was trained and hyperparameter-tuned, then benchmarked against baseline models including Linear Regression and Decision Trees. The final model was externally validated using real-world data from 11 diagnostic X-ray installations (4 polyclinics, 4 general hospitals, and 3 diagnostic centers) across Myanmar. The best-performing model was deployed as a free open-access web application on Hugging Face Spaces. The optimized Random Forest model achieved excellent performance on the synthetic test set (MAE = 0.056 cm, RMSE = 0.156 cm, R² = 0.9996), markedly outperforming the best baseline model (Decision Tree, MAE = 0.615 cm). Feature importance analysis showed that tube voltage (kVp) and brick density were the dominant predictors, contributing 53.2% and 37.8% of the total importance, respectively. External validation on independent real clinical data yielded a mean absolute error of 3.22 cm, which remains within conservative safety margins for shielding design. The model also indicated that conventional 9-inch (22.9 cm) brick walls are frequently inadequate for typical clinical workloads in Myanmar. This study demonstrates that a well-trained Random Forest model can provide highly accurate and rapid predictions of X-ray shielding requirements. Through successful validation with real clinical data from a low-resource setting and the provision of an open-source web application, the proposed approach offers a practical and accessible tool for radiation safety officers and facility designers in Myanmar and similar healthcare environments.

Keywords

Supporting Institution

Department of Atomic Energy, Myanmar and Ascend International Preparatory College,

Project Number

This research did not receive funding from any specific grant or project number.

Ethical Statement

This study was conducted using synthetic data generated from NCRP 147 formulas and anonymized inspection records from the Department of Atomic Energy, Myanmar. No human participants, animal subjects, or personal data were involved. Formal ethical review and approval were therefore waived.

Thanks

The authors gratefully acknowledge the support and resources provided by the Department of Atomic Energy, Myanmar. We extend our sincere appreciation to Ascend International Preparatory College for core algorithm support and to the Radiology Department of Yangon General Hospital for providing X‑ray facility data. We also thank our colleagues in the Ministry of Science and Technology for their insightful discussions and technical assistance. Special thanks are due to the open‑source scientific community for the Python libraries that enabled this work. Any errors or omissions remain solely the responsibility of the authors.

References

  1. [1] United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), Sources and effects of ionizing radiation, New York, United Nations, 2018.
  2. [2] K. H. Ng and M. M. Rehani, X-ray imaging goes digital: benefits and risks, Biomedical Imaging and Intervention Journal, 4(2), e25, 2008.
  3. [3] G. Sánchez-Barroso, M. Botejara-Antúnez, J. García-Sanz-Calcedo, and F. Zamora-Polo, A life cycle analysis of ionizing radiation shielding construction systems in healthcare buildings, Journal of Building Engineering, 41, 102387, 2021.
  4. [4] N. Z. J. Jamaluddin, M. M. Bani-Ahmad, N. N. Z. Azman, and R. Ramli, Eggshell-enhanced composites: Innovative radiation shielding materials for diagnostic X-ray applications, Radiation Physics and Chemistry, 224, 112076, 2024.
  5. [5] A. Dwiyanto, G. Hardiman, and W. Budi, Comparative study of the absorbed dose of secondary shield wall elements in a digital radiography room, International Journal of Scientific and Research Publications, 8(6), 352-358, 2018.
  6. [6] A. S. Wibowo, E. Cahyono, R. S. Iswari, K. A. Amin, and M. Jannah, Comparison of the effectiveness of radiation shield wall between lead-layers and plastering brick-layers, Jurnal Riset Kesehatan, 11(2), 95-102, 2022.
  7. [7] M. Haider, S. Shill, Q. Mohammad, R. Nizam, and M. Akramuzzaman, Shielding calculation based on NCRP methodologies for some diagnostic x-ray facilities in Bangladesh, International Conference on Physics for Sustainable Development, 2014.
  8. [8] Department of Atomic Energy, Myanmar, Annual inspection reports of diagnostic X-ray facilities (Internal), Nay Pyi Taw: DAE, 2023.

Details

Primary Language

English

Subjects

Clinical Sciences (Other), Nuclear Physics, Reactor Technology

Journal Section

Research Article

Publication Date

July 16, 2026

Submission Date

May 6, 2026

Acceptance Date

June 17, 2026

Published in Issue

Year 2026 Volume: 10 Number: 2

APA
Oo, Z. L., & Laı, T. W. (2026). Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147. Journal of Nuclear Sciences, 10(2). https://doi.org/10.59474/nuclear.2023.69
AMA
1.Oo ZL, Laı TW. Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147. Journal of Nuclear Sciences. 2026;10(2). doi:10.59474/nuclear.2023.69
Chicago
Oo, Zaw Lin, and Theint Win Laı. 2026. “Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147”. Journal of Nuclear Sciences 10 (2). https://doi.org/10.59474/nuclear.2023.69.
EndNote
Oo ZL, Laı TW (July 1, 2026) Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147. Journal of Nuclear Sciences 10 2
IEEE
[1]Z. L. Oo and T. W. Laı, “Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147”, Journal of Nuclear Sciences, vol. 10, no. 2, July 2026, doi: 10.59474/nuclear.2023.69.
ISNAD
Oo, Zaw Lin - Laı, Theint Win. “Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147”. Journal of Nuclear Sciences 10/2 (July 1, 2026). https://doi.org/10.59474/nuclear.2023.69.
JAMA
1.Oo ZL, Laı TW. Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147. Journal of Nuclear Sciences. 2026;10. doi:10.59474/nuclear.2023.69.
MLA
Oo, Zaw Lin, and Theint Win Laı. “Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147”. Journal of Nuclear Sciences, vol. 10, no. 2, July 2026, doi:10.59474/nuclear.2023.69.
Vancouver
1.Zaw Lin Oo, Theint Win Laı. Development and Validation of a Random Forest Model for X-Ray Shielding Thickness Prediction in Myanmar Healthcare Facilities Based on NCRP Report 147. Journal of Nuclear Sciences. 2026 Jul. 1;10(2). doi:10.59474/nuclear.2023.69