Cloud Optimized Enterprise Healthcare Analytics Using AI Genetic Algorithms Blockchain and Apache Infrastructure
DOI:
https://doi.org/10.15662/IJARCST.2024.0705013Keywords:
Enterprise Healthcare Analytics, Artificial Intelligence, Genetic Algorithms, Blockchain, Apache Hadoop, Apache Spark, Hyperledger Fabric, Cloud Computing, Big Data, Distributed Systems, Secure Healthcare Data, Clinical Decision SupportAbstract
The rapid evolution of enterprise healthcare systems has led to the generation of massive volumes of heterogeneous data, including electronic health records, medical imaging, insurance claims, genomic data, and real-time patient monitoring streams. Efficient processing, secure sharing, and intelligent analysis of such data require scalable, optimized, and secure computational frameworks. This research proposes a cloud-optimized enterprise healthcare analytics architecture integrating Artificial Intelligence (AI), Genetic Algorithms (GA), Blockchain technology, and Apache-based distributed infrastructure. The framework leverages Apache Hadoop and Apache Spark for large-scale distributed data processing, while AI-driven machine learning models perform predictive analytics and clinical decision support. Genetic Algorithms enhance model performance through feature selection, hyperparameter optimization, and intelligent resource allocation. Blockchain platforms such as Hyperledger Fabric ensure secure, tamper-proof data exchange and decentralized trust among healthcare stakeholders. The integrated approach improves scalability, security, interoperability, and computational efficiency across enterprise healthcare networks. Experimental evaluation demonstrates improved predictive accuracy, reduced latency, enhanced data integrity, and optimized cloud resource utilization. The proposed system supports enterprise-level healthcare analytics, secure patient data sharing, and intelligent hospital management in modern cloud ecosystems.
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