Published Paper Details:

A significant advancement in the functionality of medical devices with in biomedical engineering is represented by the integration of cloud computing with machine learning (ML). To enhance the medical research and patient care, biomedical big data is abruptly increasing due to it, combine the large-scale and different healthcare data with modern computing and intelligent technologies. It has the inclusion of advancements in genomics, medical imaging, wearable sensors and electronic health record systems. Traditional computational systems are not proficient to process such large-scale and heterogeneous datasets. Cloud computing provides the infrastructure for storing and managing huge volumes of healthcare data generated by medical devices, facilitating easy access and processing from anywhere, anytime. Meanwhile, medical devices analyze this data, identify patterns and make predictions, enhancing diagnostic accuracy and patient outcomes with machine learning algorithms. Cloud computing integrate with high-performance machine learning (HPML) provides scalable storage, parallel processing and intelligent analytics for biomedical applications. This paper presents an introspective study of cloud architectures, machine learning models, healthcare applications, benefits, limitations and future tendencies in biomedical big data analytics.