Abstract
The integration of cloud computing and machine learning in healthcare platforms has revolutionized the delivery of medical services, offering scalable solutions for data storage, processing, and analysis. This study presents an overview of various cloud-based healthcare platforms, focusing on the effectiveness of machine learning approaches in enhancing patient care and operational efficiency, and compares the performance of different machine learning models employed in the platforms for diverse healthcare applications. The findings provide insights into the strengths and limitations of existing cloud-based healthcare solutions, guiding healthcare providers and policymakers in selecting optimal platforms for improved patient outcomes and resource utilization.
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