Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. 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. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.
Leaving children unattended in car for few moments, especially in hot atmosphere can cause a catastrophic tragedy to occur. Even young children can die of hyperthermia after being left in hot cars for a period of time. This paper presents HACC (Hyperthermia Alert for Children in Cars) system to help in preventing tragic child death caused by hyperthermia using detection and control system inside car. HACC system has been accomplished by developing a phone application and a surveillance system connected together to monitor temperature and presence of a child inside car. The system starts to measure temperature inside car via temperature sensor. At the same time, it checks constantly the presence of child inside car via motion sensor. When system detects the presence of a child and temperature inside car reaches unsafe limit, it alerts caregiver via smartphone application and allows him/her at the same time to take an action and open windows remotely. If there is no response from caregiver, system itself reacts and windows are opened automatically.
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