Machine Learning-Based Early Detection of COVID-19 Through Real-Time Health Data Analysis

IoT-based COVID-19 Early Detection System Based on Real-Time Health Data Collected Using Sensors and Wearables for Predicting Potentials Using Machine Learning Algorithms A three-layered architecture is followed by the proposed system: detection, processing, and cloud layers. The detection layer collects data pertaining to temperature, heart rate, and oxygen level, while Processing Layer uses various machine learning models such as linear regression and logistic regression for risk detection of COVID-19. The Cloud Layer integrates data storage and facilitates the continuous updating of models. Of all the models that were tested, Linear Regression showed the highest accuracy (98.56%), precision (99.2%), recall (99.2%), and F1 score (99.4%). This integrated system is proactive and efficient in early detection of COVID-19, which increases the diagnostic accuracy and provides timely interventions.