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dc.contributor.authorBonyo, Lesley
dc.date.accessioned2022-04-19T08:40:47Z
dc.date.available2022-04-19T08:40:47Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/11071/12757
dc.descriptionA Thesis Submitted to the School of Computing and Engineering Sciences in Partial Fulfilment for the Requirement of the Degree of Master of Science in Information Technology of Strathmore Universityen_US
dc.description.abstractCoronavirus disease 2019 (COVID-19), which was declared a pandemic by the World Health Organization, is a respiratory illness caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). With no specific treatment against SARS-CoV-2, early detection of COVID-19 is vital to effective tracking and management of the disease. For this reason, several diagnostic strategies have been implemented to identify COVID-19 infection, to test for past infection and immune response. These include molecular tests such as RT-PCR, antibody tests and medical image analysis. While the RT-PCR is the gold standard test for confirming the COVID-19 infection, it requires specialized labs and is time consuming. As an alternative, Chest X-Ray and CT images using deep learning algorithms have been used. However, because of harmful radiation doses these approaches cannot be relied on for patients’ screening. Hence, there is a need for a less expensive, more accessible, and faster detection model to identify COVID-19 disease. Physiological data such as temperature and oxygen saturation can aid in COVID-19 detection and monitoring of COVID-19 patients. The symptoms for a person who is indicative of Covid-19 include shortness of breath, abnormal heartbeat, and abnormalities in lung function like the symptoms of pneumonia. Further, there is a target oxygen saturation range for patients with COVID-19 recommended by the National Institutes of Health. Such data can be continuously collected to monitor health of individuals using Internet of Things (IoT). The research tested various machine learning algorithms and implemented a low cost IoT based system with a KNN model which produced the best results. The KNN model, based on monitored oxygen saturation levels, heart rate and other COVID-19 symptoms, made predictions of person’s health based on their possibilities for COVID-19 infection an accuracy of 66.67 percent. This can aid in early detection of COVID-19 symptoms to influence early testing of individuals and to assist hospitals in remote monitoring of symptoms in patients who have contracted the virus.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectCOVID-19en_US
dc.subjectIoTen_US
dc.subjectPulse Oximeteren_US
dc.subjectKNNen_US
dc.titleIoT pulse oximetry model for early detection of COVID-19en_US
dc.typeThesisen_US


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