Machine learning and Internet of Things in early detection of COVID-19 pneumonia

This chapter examines the role of machine learning (ML) and Internet of Things (IoT) technologies in early diagnosis of COVID-19 pneumonia. Given the limitations of traditional methods such as the need for complex equipment, delayed results, and inadequate accessibility in underserved areas, integrating IoT sensors for real-time monitoring of vital signs (e.g., body temperature, oxygen saturation, and heart rate) with ML algorithms enables faster and more accurate identification of patterns associated with pulmonary infection. Deep learning-based models (e.g., convolutional neural networks) have demonstrated up to 99% accuracy in diagnosing COVID-19 pneumonia by analyzing radiological images and clinical data. Cloud-based intelligent systems also enable processing of large amounts of data and sharing of results in a secure environment. Key challenges include data privacy, equipment standardization, and the need for robust infrastructure for global scale deployment. These technologies have not only improved pandemic management but also offer a vision for smarter responses to future health crises.