Abstract
Machine learning is one of the hottest topics in e-healthcare and has a big social impact. It is challenging to build and develop an efficient and successful e-healthcare system without machine learning. Machine learning's primary function is to analyse healthcare data, which is gathered from various sources and can be both homogeneous and heterogeneous. Therefore, creating an effective machine learning algorithm that functions with both homogeneous and heterogeneous data is a difficult task. On the other hand, it is noted that a sizable number of health-related products, such as fitness bands, smart watches, and sensors, are being developed, and the majority of people use these items to track their own health. Each person's health data is also gathered by these devices, and a machine learning algorithm is integrated into the data to identify any strange patterns or behaviours. These gadgets capture any aberrant behaviour or pattern, and an alarm message is sent to the individual. Thus, machine learning (ML) presents a promising technique for cutting healthcare device costs and describing doctor-patient relationships at a higher level.
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