arXiv AI July 29, 2026 By Md Zahid Hasan Ontor, Md Al Amin, Anik Dev Nath, Bikash Kumar Paul

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

arXiv:2607. 25348v1 Announce Type: cross Abstract: Chronic Kidney Disease (CKD), characterized by the gradual loss of kidney function, remains a significant public health challenge.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 4

Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation

arXiv:2501. 16388v3 Announce Type: replace Abstract: Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem.

By Jingying Ma, Jinwei Wang, Lanlan Lu, Zhiqin Jiang, Mengling Feng, Feifei Zhang, Peng Shen, Yexiang Sun, Shenda Hong, Luxia Zhang

safety

arXiv AI

Jul 15

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability

arXiv:2607. 11963v1 Announce Type: cross Abstract: The early detection of Chronic Kidney Disease using machine learning has attracted significant interest in healthcare-related computer science.

By Mashrul Hossain, Nafesa Kibria, Fahim Shahriar

arXiv AI

Jun 29

Chronic Kidney Disease Prognosis Prediction Using Transformer

arXiv:2511. 02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure.

By Yohan Lee, Dong Gyun Kang, SeHoon Park, Sa-Yoon Park, Kwangsoo Kim

llms nlp fine-tuning efficiency multimodal safety

arXiv Machine Learning

Aug 5

FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare

arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.

By Rojalini Tripathy, Padmalochan Bera, Shreya Ghosh, Rajkumar Buyya

arXiv Machine Learning

Jul 27

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.

By Rodrigo Tertulino

benchmarks safety

arXiv Machine Learning

Jun 4

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv:2606. 04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis.

By Xixi Tian, Di Wu, Xiang Liu, Yiziting Zhu, Yujie Li, Xin Shu, Bin Yi

safety

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Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability

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