Towards the Efficient and Privacy-Aware Diagnosis of Bladder Inflammations
Corresponding author: Rashmi Ashtagi
Abstract
Conventional centralized machine learning solutions for medical diagnosis face significant limitations, such as data privacy concerns, communication overhead, and poor scalability across healthcare institutions, restricting real-time and secure diagnostic capabilities for diseases such as acute bladder inflammation. To address these challenges, this work proposes a highly accurate and privacy-preserving model that ensures scalability and efficiency while reducing training time. A Federated Logistic Regression (FLR) approach uses data from multiple clinical institutions to collaboratively train a global model without exchanging raw patient data. The FedAvg algorithm is employed to aggregate locally trained models into a centralized global model, evaluated on a dataset of urinary tract inflammations. Experimental findings reveal that the federated architecture achieves diagnostic accuracy comparable to centralized frameworks while requiring significantly fewer training iterations, resulting in a ~20× improvement in convergence efficiency without compromising patient privacy. The FLR model demonstrates a secure, realistic, and computationally feasible solution for the diagnosis of acute bladder inflammation, underscoring the transformative potential of federated learning to advance privacy-preserving medical diagnostic systems within distributed healthcare settings.
Keywords:
nephritis, Federated Learning (FL), logistic regression, distributed systems, bladder inflammationDownloads
References
F. Del Ben et al., "A fully interpretable machine learning model for increasing the effectiveness of urine screening," American Journal of Clinical Pathology, vol. 160, no. 6, pp. 620–632, Dec. 2023. DOI: https://doi.org/10.1093/ajcp/aqad099
R. Magherini, E. Mussi, Y. Volpe, R. Furferi, F. Buonamici, and M. Servi, "Machine Learning for Renal Pathologies: An Updated Survey," Sensors, vol. 22, no. 13, Jul. 2022, Art. no. 4989. DOI: https://doi.org/10.3390/s22134989
J. Czerniak and H. Zarzycki, "Application of rough sets in the presumptive diagnosis of urinary system diseases," in Artificial Intelligence and Security in Computing Systems, Boston, MA, USA, 2003, pp. 41–51. DOI: https://doi.org/10.1007/978-1-4419-9226-0_5
S. Farashi and H. E. Momtaz, "Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables," BMC Medical Informatics and Decision Making, vol. 25, no. 1, Jan. 2025, Art. no. 13. DOI: https://doi.org/10.1186/s12911-024-02819-2
M. K. S. Ma'aitah, A. Helwan, and A. Radwan, "Urinary Bladder Acute Inflammations and Nephritis of the Renal Pelvis: Diagnosis Using Fine-Tuned Large Language Models," Journal of Personalized Medicine, vol. 15, no. 2, Jan. 2025, Art. no. 45. DOI: https://doi.org/10.3390/jpm15020045
S. Li, X. Li, Y. Wang, J. Shen, G. U. Nneji, and H. N. Monday, "Dependable AI Machine Learning Models for the Prediction of Urinary System Diseases," in 2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE), Hefei, China, Sep. 2024, pp. 366–370. DOI: https://doi.org/10.1109/ICACTE62428.2024.10871567
L. E. Lamb et al., "Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions," Urology, vol. 189, pp. 19–26, Jul. 2024. DOI: https://doi.org/10.1016/j.urology.2024.03.043
S. Baijwan and A. Dhyani, "Performance Analysis of an Inflammatory Process in the Bladder Cystitis Development using Machine Learning Approach," in 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, Feb. 2023, pp. 01–06. DOI: https://doi.org/10.1109/ICICACS57338.2023.10099872
M. A. Mohammed, M. K. A. Ghani, A. Lakhan, B. AL-Attar, and W. Khaled, "Federated Learning-Driven IoT and Edge Cloud Networks for Smart Wheelchair Systems in Assistive Robotics," Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 1, Mar. 2025. DOI: https://doi.org/10.52866/2788-7421.1241
A. Lakhan et al., "FDCNN-AS: Federated deep convolutional neural network Alzheimer detection schemes for different age groups," Information Sciences, vol. 677, Aug. 2024, Art. no. 120833. DOI: https://doi.org/10.1016/j.ins.2024.120833
M. A. Mohammed et al., "Federated-Reinforcement Learning-Assisted IoT Consumers System for Kidney Disease Images," IEEE Transactions on Consumer Electronics, vol. 70, no. 4, pp. 7163–7173, Nov. 2024. DOI: https://doi.org/10.1109/TCE.2024.3384455
J. Czerniak, "Acute Inflammations." UCI Machine Learning Repository, 2003.
T. Haifley, "Linear logistic regression: an introduction," in IEEE International Integrated Reliability Workshop Final Report, 2002., Lake Tahoe, CA, USA, 2002, pp. 184–187. DOI: https://doi.org/10.1109/IRWS.2002.1194264
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, May 2020. DOI: https://doi.org/10.1109/MSP.2020.2975749
L. Guo, M. Li, S. Xu, and F. Yang, "Application of Stochastic Gradient Descent Technique for Method of Moments," in 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), Singapore, Aug. 2020, pp. 97–98. DOI: https://doi.org/10.1109/ICCEM47450.2020.9219400
S. Gunter and H. Bunke, "Optimizing the number of states, training iterations and Gaussians in an HMM-based handwritten word recognizer," in Seventh International Conference on Document Analysis and Recognition, 2003, Proceedings., Edinburgh, UK, 2003, vol. 1, pp. 472–476. DOI: https://doi.org/10.1109/ICDAR.2003.1227710
U. Ruby, P. Theerthagiri, I. J. Jacob, and Y. Vamsidhar, "Binary cross entropy with deep learning technique for Image classification," International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 4, pp. 5393–5397, Aug. 2020. DOI: https://doi.org/10.30534/ijatcse/2020/175942020
Downloads
How to Cite
License
Copyright (c) 2025 Rashmi Ashtagi, Ranjeet Bidwe, Sangita Jaybhaye, Nilesh J. Uke, Jinay Jain, Uday Jaju, Khushi Agarwal, Ajinkya Kalamkar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
