Intelligent Mobile Cloud Platform for Monitoring Patients of COVID-19 in Their Home-Quarantines - MuthanaHamza Hussein Al_Zuhairi , Dr. Maha A. Al-Bayati (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Currently the world is going through a pandemic caused by Covid-19, the World Health Organization recommends to stay isolated from the rest of the people. This research shows the development of a prototype based on the intelligent mobile cloud computing (MCC), which aims to measure three very important aspects: heart rate, blood oxygen saturation and body temperature, these will be measured through sensors that will be connected to a NodeMCU module that integrates a Wi-Fi and Bluetooth module, which will transmit the data by using (MQTT) protocol to an IoT platform (Ubidots) through which the data can be displayed, achieving real-time monitoring of the vital signs of the patient Confirmedof Covid-19 in home quarantine and sending alerts to health centers for critical cases,In addition to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Telemedicine center (that uses machine learning algorithms), Health physicians (doctor), and Mobile Cloud Computing infrastructure. To quickly classifyConfirmed coronaviruses cases and predict critical cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. An experiment was conducted to test these eight algorithms on a real COVID-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 %. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate classification of Confirmed cases of COVID-19 to Stable and critical situations, and the framework would then document the treatment response for each patient who has contracted the virus”.