Sensors, Vol. 21, Pages 6750: Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

Sensors, Vol. 21, Pages 6750: Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset Sensors doi: 10.3390/s21206750 Authors: Mubashir Rehman Raza Ali Shah Muhammad Bilal Khan Syed Aziz Shah Najah Abed AbuAli Xiaodong Yang Akram Alomainy Muhmmad Ali Imran Qammer H. Abbasi The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research