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<p>Healthcare analytics aims to derive profound insights and make accurate predictions, which makes preserving critical patients’ data privacy. To solve this problem, this research implements a Federated Learning Framework to execute Whale Optimization for Health Privacy Preservation Analytics (WHOPPA) of healthcare records. Employing the Whale Optimization Algorithm, a metaheuristic optimization approach is modelled with the social behaviour of humpback whales. The primary purpose of this methodology is to strive for optimum performance while retaining privacy through federated learning in various healthcare sectors. The WHOPPA framework will be constructed to adapt to varying data and ensuring privacy requirements by healthcare organization with advanced analytics tools. Proposed WHOPPA is benchmarked against PSO-based, Genetic Algorithm-based, and Ant Colony Optimization-based models. The WHOPPA system demonstrates efficiency in privacy preservation and optimization of datasets, attaining 87% scalability and 90% convergence. It is proficient at handling different kinds of data distributions with scores of 91% and 93%. In addition, it realizes communication efficiency of 92% and security of 88%, which means that the balance of data sharing and privacy protection is achieved. Computational efficiency of WHOPPA is 89% which shows the algorithm is comprehensive, optimal, and scalable in healthcare data analytics. This work sets out to construct a technology-driven proactive healthcare system with robust safety features strengthening the system reinforced by the data frameworks.</p>

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WHOPPA: Whale Optimization for Privacy-Preserving Healthcare Data Analytics in Federated Learning

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • M. Manimaran,
  • V. Dhilipkumar

摘要

Healthcare analytics aims to derive profound insights and make accurate predictions, which makes preserving critical patients’ data privacy. To solve this problem, this research implements a Federated Learning Framework to execute Whale Optimization for Health Privacy Preservation Analytics (WHOPPA) of healthcare records. Employing the Whale Optimization Algorithm, a metaheuristic optimization approach is modelled with the social behaviour of humpback whales. The primary purpose of this methodology is to strive for optimum performance while retaining privacy through federated learning in various healthcare sectors. The WHOPPA framework will be constructed to adapt to varying data and ensuring privacy requirements by healthcare organization with advanced analytics tools. Proposed WHOPPA is benchmarked against PSO-based, Genetic Algorithm-based, and Ant Colony Optimization-based models. The WHOPPA system demonstrates efficiency in privacy preservation and optimization of datasets, attaining 87% scalability and 90% convergence. It is proficient at handling different kinds of data distributions with scores of 91% and 93%. In addition, it realizes communication efficiency of 92% and security of 88%, which means that the balance of data sharing and privacy protection is achieved. Computational efficiency of WHOPPA is 89% which shows the algorithm is comprehensive, optimal, and scalable in healthcare data analytics. This work sets out to construct a technology-driven proactive healthcare system with robust safety features strengthening the system reinforced by the data frameworks.