Amazon Dash button is one of the smart outcomes of IoT technology that started to show in US market in April 2015 to enable users to shop for their frequently requested items by a press of a Wi-Fi connected button. It plays an important role in data gathering regardless of time and environment. Internet of Things (IoT) is an emerging technology with a wide range of applications including smart homes, cars, offices and so on. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. ![]() This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. The variation in prediction results looms large in the clinical decision-making process. Due to the predictive results varying, this might impact the overall results. Individual ML algorithms perform differently on different datasets. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data.
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