ABSTRACT
In recent times, the healthcare industry has witnessed a rapid surge in popularity with the advent of the Internet of Medical Things (IoMT). This novel technology integrates the Internet of Things with telemedicine and telehealth, ushering in a new era of patient care. Machine Learning (ML) and Deep Learning have merged as indispensable tools in the medical sector, facilitating the analysis of vast data sets for early disease detection and the enhancement of patient healthcare. The creation of specific and multidimensional data sets assumes a pivotal role in optimizing the performance of ML algorithms. IoMT allows seamless real-time data sharing among medical equipment and healthcare devices, thereby generating large volumes of data for ML applications. However, the interconnection of disparate components across various locations in IoMT introduces significant security challenges to both the network and data sharing. In this context, the integration of intelligent security systems using ML algorithms becomes imperative. This chapter delves into the significance of ML algorithms for decision support systems in the IoMT environment, emphasizing their potential to revolutionize healthcare practices.
