Machine Learning Based Remote Health Care Data Monitoring and Prediction System Using IoMT

One of the most rapidly developing technologies is telemedicine data monitoring systems, which employ the Internet of Things (IoT) and machine learning (ML) to track and forecast patient health. This technology enables personalized healthcare to enhance health outcomes and offer individualized care and intervention metrics. It is challenging for modern healthcare to continuously monitor people's health statuses and foresee potential health issues. Traditional medical treatment frequently relies on regular checkups and typing in data manually, which may not be enough to detect minor health issues or take preventative action in emergencies. The large volume of patient-generated health data must be reliably and effectively examined to improve patient care significantly. Internet of Medical Things (loMT) and ML algorithms can transform medicine and raise the bar for individualized healthcare. To explore disease monitoring and prediction advancements, the system attempts to bring together IoT technologies, ML, and the healthcare sector. The critical outcome parameter of the study is the effectiveness of the ML model incorporated in this model, which is assessed using metrics like recall, sensitivity, error rate, and F-score. These variables allow us to determine model predictions' precision, effectiveness, and temporal complexity, giving healthcare professionals helpful information.