What question did this study set out to answer?

To develop a predictive model that maintains data privacy while achieving high accuracy in patient care.

February 13, 2026

Predictive modelling for patient care using federated learning systems combined with a private blockchain data environment and using ranked set sampling

Key Points

To develop a predictive model that maintains data privacy while achieving high accuracy in patient care.
Integrated federated learning and deep learning with a private blockchain.
Employed ranked set sampling to prioritize informative samples for model training.
Tested the model on various cancer datasets, including breast cancer and Hodgkin Lymphoma.
Achieved a test loss of 0.29 and an accuracy of 93.75% on the breast cancer dataset.
Obtained 85% accuracy and an AUC of 0.91 on the Hodgkin Lymphoma dataset, demonstrating strong performance.
Validated the model effectively on a lung cancer dataset, confirming robustness across heterogeneous data.

Abstract

Abstract Recent advancements in information technology have enhanced patient care, yet data breaches in healthcare still pose major privacy and security challenges. This study introduces a predictive model designed to safeguard sensitive medical data while maintaining high predictive accuracy by integrating blockchain, deep learning, federated learning (FL), and ranked set sampling (RSS). The model employs a long short-term memory network within a FL framework and incorporates a private blockchain for secure, decentralized, and tamper-resistant data management. To improve efficiency, RSS is used to prioritize the most informative samples before model training. The model was first tested on a breast cancer dataset, achieving a test loss of 0.29, an accuracy of 93.75%, and an AUC of 0.94, showing excellent classification performance. Further validation on a Hodgkin Lymphoma dataset, characterized by class imbalance, yielded 85% accuracy and an AUC of 0.91, confirming strong discriminative ability. The model was also validated using a lung cancer dataset. These results highlight the model’s robustness in handling heterogeneous, imbalanced clinical data. By combining FL, blockchain, and RSS, the proposed system ensures data privacy, computational efficiency, and scalability, making it a reliable and secure solution for modern healthcare applications.

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