Health condition prediction and covid risk detection using healthcare 4.0 techniques

Abstract

Healthcare 4.0 is one of the emerging concepts that has grabbed the interest among researchers as well as the medical sector. Using the Internet of Things (IoT) and sophisticated communication technologies, it is now possible to monitor the patient from a remote area. In this paper, we design a remote health monitoring system using IoT and Machine Learning (ML) to determine the health condition of a patient. Supervised ML algorithms along with a time-series model such as Seasonal Autoregressive Integrated Moving Average (SARIMA) model are applied on the gathered data from IoT medical sensors to predict the health status of a patient. We consider a use-case of covid and compared it with our sensor data by applying the unsupervised ML algorithm, Long Short Term Memory (LSTM) along with a stochastic model, namely Markov Model to detect the risk of getting covid for a particular patient. LSTM with Markov model provides better results for detection with root mean squared error (RMSE) of 0.18 as against the RMSE of 0.45 obtained with only LSTM. We further design an optimization algorithm using “fuzzy logic” that attains optimum results in detecting the risk of getting covid.