Prediction of Abnormality Using IoT and Machine Learning

Vital signs indicators like temperature, heart rate, and oxygen saturation should be examined periodically, as these are the root cause of any medical diagnosis. Any deviations from the normal range indicate that the person needs an immediate medical check-up and is on the edge of facing some medical issues later. Thus, monitoring these vitals periodically can help patients from the risk of mortality. The goal of this research is to forecast a person’s abnormality using machine learning and IoT (Internet of Things) algorithms for decisiveness. The prototype was built using three sensors, MAX30100 sensor (for SpO2), REES52 heartbeat sensor, and LM35 temperature sensor along with Arduino UNO, and ESP 8266. The bio signal data from these sensors were collected using Arduino UNO, stored in a local PC, and uploaded to the cloud using API protocol in Thingspeak (IoT platform). These data were also retrievable for further diagnosis. Support vector machine (SVM), a machine learning method, is used to predict if a patient is abnormal or not. SVM learns the threshold ranges for each parameter as well as the associated goal value from the datasets.