Early prediction in AI-enabled IoT environment

Abstract

IoT is intelligent sensors and actuators which assemble to form an IoT device. The algorithms employed make the system make up a wise decision. These systems can use artificial Intelligence algorithms to make intelligent decisions. The previous work employs devices that compute normal from abnormal heart rates. These devices are intelligent machines that are carried with the individual. They are also used to calculate the ECG of the personnel. This information understands the behavior of the personnel. The knowledge is sensed and passed to the devices using the Bluetooth technique. This data segment into healthy or unwell being sections. The processing amalgamates transformation, conversion w.r.t format, and section labeling. The iforest approach excludes the outliers from the data set. The suggestion improves the previous work by predicting the abnormality before in hand by 17.5%. Many lives can be saved, and will help improve their lives by adopting this method.