Federated Learning Based Prediction of Chronic Kidney Diseases
Abstract
Chronic Kidney diseases are increasing at an alarming rate in present days. The prediction of kidney disease consume more time as it involves a lot of laboratory tests. Traditionally, the health care domain relies on the centralized data, which may lead the system to have significant risks and difficulties. The proposed model involves uses federated learning, a machine learning technique that involves training an algorithm on multiple decentralized data. The model uses image processing technique, where the scanned image of the kidney is given as the input. The image, upon various pre-processing techniques identifies the affected area of the kidney. The affected area is also marked in color and the values of accuracy, efficiency, specificity and sensitivity are found to be higher than the existing systems. The output of the single image is again given to training dataset to increase the accuracy precisely, which is a part of the federated learning.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Training with a single image increased the accuracy of the federated learning model for chronic kidney disease prediction.
“Increased accuracy through training with a single image.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [88]
Cites
Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods
Polat, H., Mehr, H. D., & Çetin, A. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0703-x · Cited by 299 · see citing works
Performance Analysis of Data Mining Classification Techniques to Predict Diabetes
Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.04.016 · Cited by 291 · see citing works
Chandel, K., Kunwar, V., Sabitha, A. S., et al. (2016) · CSI Transactions on ICT · doi:10.1007/s40012-016-0100-5 · Cited by 117 · see citing works
Applying Machine Learning Techniques for Predicting the Risk of Chronic Kidney Disease
Padmanaban, K. R. A., & Parthiban, G. (2016) · Indian Journal of Science and Technology · doi:10.17485/ijst/2016/v9i29/93880 · Cited by 87 · see citing works
Predicting Chronic Kidney Failure Disease Using Data Mining Techniques
Boukenze, B., Haqiq, A., & Mousannif, H. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-1627-1_55 · Cited by 50 · see citing works
CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets39692 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
Pan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70128 · Cited by 19 · see citing works
- Authors
- Nandhini, J. M., Joshi, S., & Anuratha, K.
- Affiliations
- Sri Sai Ram Institute of Technology
- Venue
- 2022 1st International Conference on Computational Science and Technology (ICCST)
- Publisher
- IEEE
- Published
- 2022-11-09
- Pages
- 1-6
- OpenAlex
- W4320801995
- Cited by
- 8
- References
- 6
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 2
- FWCI
- 1.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- 2022 1st International Conference on Computational Science and Technology (ICCST) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- chronic kidney disease, federated learning, kidney disease prediction, image segmentation, decentralized data, medical image processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
