Enterprise Healthcare Decision Intelligence Using Machine Learning Genetic Algorithms and Blockchain in Distributed Cloud Environments
DOI:
https://doi.org/10.15662/IJEETR.2024.0605017Keywords:
Enterprise Healthcare, Decision Intelligence, Machine Learning, Genetic Algorithms, Blockchain, Distributed Cloud Computing, Apache Hadoop, Apache Spark, Hyperledger Fabric, Predictive Analytics, Secure Healthcare SystemsAbstract
Enterprise healthcare systems generate vast volumes of heterogeneous data from electronic health records, medical imaging, laboratory systems, wearable devices, insurance claims, and telemedicine platforms. Transforming this complex data into actionable decision intelligence requires scalable, secure, and optimized analytical frameworks. This study proposes an enterprise healthcare decision intelligence architecture that integrates Machine Learning (ML), Genetic Algorithms (GA), and Blockchain within distributed cloud environments powered by Apache Hadoop and Apache Spark. Machine Learning models enable predictive diagnostics, risk stratification, and operational forecasting. Genetic Algorithms enhance analytical performance through feature selection, hyperparameter optimization, and dynamic resource allocation. Blockchain technology, implemented via Hyperledger Fabric, ensures secure, transparent, and tamper-proof data exchange among healthcare stakeholders. The distributed cloud infrastructure supports scalability, fault tolerance, and real-time analytics across geographically dispersed healthcare enterprises. Experimental evaluation demonstrates improved predictive accuracy, reduced latency, optimized cloud resource utilization, and enhanced data integrity compared to traditional enterprise analytics systems. The proposed framework establishes a secure and intelligent decision-support ecosystem capable of addressing the growing complexity, privacy requirements, and performance demands of modern healthcare enterprises.
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