Journal of Industrial Engineering and Management Studies

Utilizing Biometric Authentication to Prevent Private Sharing of Physician Information for Prescription System Access

Document Type : Original Article

Authors

Ahrar Hosseini 1
Behrooz Khalil Loo 2
Amir Aghsami 3

1 National Center for Health Insurance Research, Tehran, Iran

2 Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA

3 School of Industrial Engineering, K. N. Toosi University of Technology

https://doi.org/10.22116/jiems.2025.467591.1568
Abstract
In the landscape of healthcare, ensuring the accuracy and security of prescription processes is crucial for maintaining patient safety and upholding ethical standards. This paper presents a novel biometric authentication framework designed to address the vulnerabilities in traditional authentication methods such as passwords and codes, which are prone to misuse. By integrating fingerprint and iris recognition, the proposed multi-modal system provides a robust solution to prevent unauthorized access to prescription data. This study collected biometric data from 600 doctors, comprising 600 fingerprint images and 1200 iris images, to rigorously evaluate the system’s performance. Detailed information about the CNN architecture, including layers, activation functions, and loss functions, is provided. The model's effectiveness was measured using comprehensive metrics such as accuracy, precision, recall, and F1-Score, demonstrating a significant improvement over existing methods. Furthermore, a statistical analysis was conducted to verify the reliability of the results, with comparisons drawn against baseline methods. The findings underscore the importance of enhancing biometric authentication systems and contribute to the development of secure and reliable identity verification solutions across the healthcare sector. This research not only bolsters the security of prescription processes but also reinforces the ethical principles guiding medical practice, offering a significant step forward in preventing fraud in healthcare systems.

Keywords

Biometric authentication
Fingerprint recognition
Iris recognition
Multi-modal authentication
Identity verification
Kurgan, L. A., Cios, K. J., & Dick, S. (2006). Highly scalable and robust rule learner: performance evaluation and comparison. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36(1), 32-53.
Li, N., Wang, Z., Yang, X., Zhang, Z., Zhang, W., Sang, S., & Zhang, H. (2024). Deep‐Learning‐Assisted Thermogalvanic Hydrogel E‐Skin for Self‐Powered Signature Recognition and Biometric Authentication. Advanced Functional Materials, 2314419.
Lucia, C., Zhiwei, G., & Michele, N. (2023). Biometrics for Industry 4.0: a survey of recent applications. Journal of Ambient Intelligence and Humanized Computing, 14(8), 11239-11261.
Mamat, N., Rasam, A. R. A., Adnan, N. A., & Abdullah, I. C. (2014). GIS-based multi-criteria decision making system for determining potential site of oyster aquaculture in Terengganu. Paper presented at the 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.
Mamoudan, M. M., Forouzanfar, D., Mohammadnazari, Z., Aghsami, A., & Jolai, F. (2023). Factor identification for insurance pricing mechanism using data mining and multi criteria decision making. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8153-8172. doi:10.1007/s12652-021-03585-z
Mamoudan, M. M., Jafari, A., Mohammadnazari, Z., Nasiri, M. M., & Yazdani, M. (2023). Hybrid machine learning-metaheuristic model for sustainable agri-food production and supply chain planning under water scarcity. Resources, Environment and Sustainability, 14, 100133. doi:https://doi.org/10.1016/j.resenv.2023.100133
Mantzana, V., Koumaditis, K., & Themistocleous, M. (2011). Healthcare IS interoperability: challenges and solutions. Paper presented at the International Conference on Health Informatics, HEALTHINF 2011.
Marino, C., Penedo, M. G., Penas, M., Carreira, M. J., & Gonzalez, F. (2006). Personal authentication using digital retinal images. Pattern Analysis and Applications, 9, 21-33.
Mason, J., Dave, R., Chatterjee, P., Graham-Allen, I., Esterline, A., & Roy, K. (2020). An investigation of biometric authentication in the healthcare environment. Array, 8, 100042.
Matloob, I., Khan, S., ur Rahman, H., & Hussain, F. (2020). Medical health benefit management system for real-time notification of fraud using historical medical records. Applied Sciences, 10(15), 5144.
Mousapour Mamoudan, M., Ostadi, A., Pourkhodabakhsh, N., Fathollahi-Fard, A. M., & Soleimani, F. (2023). Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market. Journal of Computational Design and Engineering, 10(3), 1110-1125. doi:10.1093/jcde/qwad039
PK, R., Khaparde, A., Bendre, V., & Katti, J. (2024). Fraud detection and prevention by face recognition with and without mask for banking application. Multimedia Tools and Applications, 1-24.
Pourkhodabakhsh, N., Mamoudan, M. M., & Bozorgi-Amiri, A. (2023). Effective machine learning, Meta-heuristic algorithms and multi-criteria decision making to minimizing human resource turnover. Applied Intelligence, 53(12), 16309-16331. doi:10.1007/s10489-022-04294-6
Roh, J.-h., Cho, S., & Jin, S.-H. (2018). Learning based biometric key generation method using CNN and RNN. Paper presented at the 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE).
Shakil, K. A., Zareen, F. J., Alam, M., & Jabin, S. (2020). BAMHealthCloud: A biometric authentication and data management system for healthcare data in cloud. Journal of King Saud University-Computer and Information Sciences, 32(1), 57-64.
Srivastava, R., & Sharma, D. K. (2022). Human Authentication Using Score Level Fusion of Face and Palm Print Biometrics. In VLSI, Microwave and Wireless Technologies: Select Proceedings of ICVMWT 2021 (pp. 55-64): Springer.
Therar, H. M., Mohammed, L. D. E. A., & Ali, A. J. (2021). Multibiometric system for iris recognition based convolutional neural network and transfer learning. Paper presented at the IOP Conference Series: Materials Science and Engineering.
Umasankari, N., Muthukumar, B. and Shanmuganathan, C., 2024. Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM. SN Computer Science, 5(4), p.337.
Vensila, C., & Boyed Wesley, A. (2024). Multimodal biometrics authentication using extreme learning machine with feature reduction by adaptive particle swarm optimization. The Visual Computer, 40(3), 1383-1394.
Venturini, L., Budd, S., Farruggia, A., Wright, R., Matthew, J., Day, T. G., . . . Hajnal, J. V. (2024). Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans. arXiv preprint arXiv:2401.01201.
Zafari, B., & Ekin, T. (2019). Topic modelling for medical prescription fraud and abuse detection. Journal of the Royal Statistical Society Series C: Applied Statistics, 68(3), 751-769.
Journal of Industrial Engineering and Management Studies
Volume 11, Issue 2
December 2024
Pages 105-122

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