Forecast Illness Risks Based on Machine Learning And The Internet of Things

Abstract

   IoT device treatment is expanding daily in the modern era, physically connecting the environment. Pneumonia also frequently affects humans, but these days it mostly affects mid-sized people due to structural modifications. One of the current abilities that may be used with IOT tactics was machine learning. Acceptable to predict the risk of diseases like pneumonia, this project emphasizes the importance of the Internet of Things (IoT) and highlights the need for further research. It proposes a new approach that uses machine learning to predict the infection by using datasets gathered with chest X-ray pictures. The use of sensors in Internet of Things devices, a Back Propagation Classifier in conjunction through a Naïve Bayesian classifier, then feature choice techniques such as the Meerkat Clan Algorithm. Certain populations are identified, and the burden of the sickness can be mitigated for those in that group. The proposed system has a 99% accuracy rate.