An Apache Spark Driven AI Framework for Enterprise Healthcare Analytics with Genetic Algorithm Optimization and Blockchain Security
DOI:
https://doi.org/10.15662/IJRAI.2024.0706037Keywords:
Enterprise Healthcare Analytics, Apache Spark, Artificial Intelligence, Machine Learning, Genetic Algorithm Optimization, Blockchain Security, Hyperledger Fabric, Distributed Cloud Computing, Big Data Analytics, Secure Healthcare SystemsAbstract
Enterprise healthcare organizations generate vast volumes of heterogeneous data from electronic health records, medical imaging systems, laboratory databases, wearable devices, and insurance platforms. Efficiently processing and securing this data while extracting actionable intelligence remains a critical challenge. This study proposes an Apache Spark–driven Artificial Intelligence framework that integrates Machine Learning (ML), Genetic Algorithm (GA) optimization, and Blockchain-based security for enterprise healthcare analytics. The framework leverages Apache Spark for distributed in-memory processing and scalable machine learning, supported by Apache Hadoop for reliable data storage. Genetic Algorithms are applied for feature selection, hyperparameter tuning, and dynamic resource optimization to enhance predictive performance and computational efficiency. Blockchain technology, implemented through Hyperledger Fabric, ensures secure, transparent, and tamper-proof data exchange among healthcare stakeholders. The integrated architecture improves prediction accuracy, reduces processing latency, enhances data integrity, and optimizes cloud resource utilization. Experimental evaluation demonstrates significant performance gains compared to conventional healthcare analytics systems. The proposed framework provides a scalable, secure, and intelligent solution for enterprise healthcare decision-making in distributed cloud environments.
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