Authors : Sandeep Partole; Vijay Shelake

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/yujatpnu

Scribd : https://tinyurl.com/mt9t2hd8

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1123

Abstract : Now a day machine learning & deep learning and artificial intelligence has major important impact on the medical healthcare and pharma industry. Which useful for identifying, monitoring and treatment of the concern medical patients. By activating the diagnosis, individual therapies, this technical methodology is significantly improvising healthcare research methodology and outcome. An improvising trend in most famous culture and traditional healthcare involves remote monitoring of patient’s health. Now a day most of the people are using advance smart devices like smart watches to track the fitness trackers, blood oxygen levels, heartbeat rate variability using different optimization and classification techniques. To collect and aggregates patients’ health information or data from the different healthcare hospitals, healthcare-based research Centre, and healthcare smart devices users a fog computing and edge computing model that are going to implemented to enhance the early prediction of the deceases. To request and reply system series is provided by our research model to obtain and analyses medical based patients data remotely by the patient’s doctors and physician.

Keywords : Convolution Neural Network, Medical Healthcare, Machine Learning, Fog-Edge Computing.

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