A novel computational method for assigning weights of importance to symptoms of COVID-19 patients.

BACKGROUND AND OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) is considered a pandemic by the World Health Organization (WHO). As of April 3, 2020, there were 1,009,625 reported confirmed cases, and 51,737 reported deaths. Doctors have been faced with a myriad of patients who present with many different symptoms. This raises two important questions. What are the common symptoms, and what are their relative importance? METHODS: A non-structured and incomplete COVID-19 dataset of 14,251 confirmed cases was preprocessed. This produced a complete and organized COVID-19 dataset of 738 confirmed cases. Six different feature selection algorithms were then applied to this new dataset. Five of these algorithms have been proposed earlier in the literature. The sixth is a novel algorithm being proposed by the authors, called Variance Based Feature Weighting (VBFW), which not only ranks the symptoms (based on their importance) but also assigns a quantitative importance measure to each symptom. RESULTS: For our COVID-19 dataset, the five different feature selection algorithms provided different rankings for the most important top-five symptoms. They even selected different symptoms for inclusion within the top five. This is because each of the five algorithms ranks the symptoms based on different data characteristics. Each of these algorithms has advantages and disadvantages. However, when all these five rankings were aggregated (using two different aggregating methods) they produced two identical rankings of the five most important COVID-19 symptoms. Starting from the most important to least important, they were: Fever/Cough, Fatigue, Sore Throat, and Shortness of Breath. (Fever and cough were ranked equally in both aggregations.) Meanwhile, the sixth novel Variance Based Feature Weighting algorithm, chose the same top five symptoms, but ranked fever much higher than cough, based on its quantitative importance measures for each of those symptoms (Fever - 75 %, Cough - 39.8 %, Fatigue - 16.5 %, Sore Throat - 10.8 %, and Shortness of Breath - 6.6 %). Moreover, the proposed VBFW method achieved an accuracy of 92.1 % when used to build a one-class SVM model, and an NDCG@5 of 100 %. CONCLUSIONS: Based on the dataset, and the feature selection algorithms employed here, symptoms of Fever, Cough, Fatigue, Sore Throat and Shortness of Breath are important symptoms of COVID-19. The VBFW algorithm also indicates that Fever and Cough symptoms were especially indicative of COVID-19, for the confirmed cases that are documented in our database.

An IoT-based framework for early identification and monitoring of COVID-19 cases.

The world has been facing the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time COVID-19 detection and monitoring system. The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, 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, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses 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 identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus.