A Smart Incubator System for Monitoring and Controlling Premature Babies' Environment Using Machine Learning

Abstract

Since technology has developed at the present time and with the emergence of the problem of many deaths of premature babies for a number of reasons including the failure to monitor the child’s health in a timely manner and 24 hours a day in an incubator system that provides a safer environment for the premature child. In this work, an incubator monitoring and control system has been designed and implemented including four sensors: SpO2, temperature, heartbeat, and respiratory (BPM). Two actuators have been used in this work to control the incubator’s environment which are ceramic warmer and a fan. A real time dataset has been extracted from the proposed incubator designed and five machine learning where evaluated to control the incubator environment which are Linear Regression (LR), Decision Tree (DT), Nave Bayes (NB), Random Forest (RF), and Deep Neural Network (DNN). Results shows that both DT and RF obtain the best results although the other algorithms show a 100% results in five performance metrics accuracy, precision, recall, F1 score, and ROC.