Prospects of Internet of Things (IoT) and Machine Learning to Fight Against COVID-19

Haque, K. F.; Abdelgawad, A.

IoT and Machine Learning has improved multi-fold in recent years and they have been playing a great role in healthcare systems which includes detecting, screening and monitoring of the patients. IoT has been successfully detecting different heart diseases, Alzheimer disease, helping autism patients and monitoring patients’ health condition with much lesser cost but providing better efficiency, reliability and accuracy. IoT also has a great prospect in fighting against COVID-19. This chapter discusses different aspects of IoT in aiding healthcare systems for detecting and monitoring Coronavirus patients. Two such IoT based models are also designed for automatic thermal monitoring and for measuring and real-time monitoring of heart rate with wearable IoT devices. Convolutional Neural Networks (CNN) is a Machine Learning algorithm that has been performing well in detecting many diseases including Coronary Artery Disease, Malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Detecting Corona positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two CNN models with different number of convolution layers and three other models based on ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. The proposed model performs with an accuracy of 97.5% and a precision of 97.5%. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.975 and F1-score of 97.5. It can be improved further by increasing the dataset for training the model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.