A Supervised Learning-Based Framework for Predicting COVID-19 in Patients

The integration of ML and loT can provide insightful details for critical decision making, automated responses, etc. Predicting future trends detecting anomalies are some the areas where being used at a rapid rate. Machine learning help decode hidden patterns in IoT data. It may complement or replace manual processes with systems that use statistically derived behavior. In healthcare, wearable sensors tracking patient activity have been continuously producing staggering amount This paper proposes an IoT-based scalable architecture COVID-19-positive patients storing processing such massive data on cloud. proposed also employs machine algorithms correct classification patients. gradient boosting classifier method early detection COVID-19 patient's body. order to make faster terms computational power, cloud computing storage.