Machine Learning and IoT-Based Automatic Health Monitoring System

The Internet of things (IoT) has made healthcare applications more accessible to the rest of the globe. On a wide scale, IoT has been employed to interconnect therapeutic aids in order to provide world-class healthcare services. The novel sensing devices can be worn to continuously measure and monitor the participants’ vital parameters. Remotely monitored parameters can be transferred to medical servers via the Internet of things, which can then be analyzed by clinicians. Furthermore, machine learning algorithms can make real-time decisions on the abnormal character of health data in order to predict disease early. This study presents a machine learning and Internet of things (IoT)-based health monitoring system to let people measure health metrics quickly. Physicians would also benefit from being able to monitor their patients remotely for more personalized care. In the event of an emergency, physicians can respond quickly. In this study, the Espressif modules 8266 are used to link health parameter sensors, which are implanted to measure data and broadcast it to a server. With the real-time data from the sensors, three statistical models were trained to detect anomalous health conditions in the patients: K-nearest neighbors (KNNs), logistic regression, and support vector machine (SVM). Due to abnormal health markers, these models uncover patterns during training and forecast disease in the subject.