Optimized Healthcare Data Processing Using Genetic Algorithms and Machine Learning on Apache Cloud Infrastructure
DOI:
https://doi.org/10.15680/IJCTECE.2024.0704011Keywords:
Healthcare Data Processing, Genetic Algorithms, Machine Learning, Apache Hadoop, Apache Spark, Cloud Computing, Big Data Analytics, Feature Selection, Predictive Modeling, Distributed Systems, Clinical Decision Support SystemsAbstract
The exponential growth of healthcare data from electronic health records, wearable devices, medical imaging, and genomic sequencing has created significant challenges in efficient data processing and decision-making. Traditional analytical approaches often struggle with scalability, optimization, and real-time performance requirements. This study proposes an optimized healthcare data processing framework that integrates Genetic Algorithms (GA) with Machine Learning (ML) techniques deployed on Apache cloud infrastructure, particularly leveraging Apache Hadoop and Apache Spark. Genetic Algorithms are employed for feature selection, hyperparameter optimization, and resource allocation, while Machine Learning models such as supervised and ensemble methods are used for predictive analytics and disease classification. The Apache ecosystem ensures distributed storage and parallel processing, enabling scalable and fault-tolerant computation across large healthcare datasets. Experimental evaluation demonstrates improved prediction accuracy, reduced computational overhead, and enhanced system scalability compared to traditional optimization approaches. The proposed framework supports real-time healthcare analytics, efficient clinical decision support systems, and intelligent hospital resource management. The integration of evolutionary computation with distributed machine learning presents a robust solution for next-generation healthcare data processing systems in cloud environments.
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