2021/10/18 by Sarwan Ali, Ali, Sarwan, Yijing Zhou +3
Computer Science · Health Professions · Medicine · #Anomaly Detection Techniques and Applications #Artificial Intelligence in Healthcare #Artificial intelligence #Big data #COVID-19 diagnosis using AI #Categorical variable #Computer science #Coronavirus disease 2019 (COVID-19) #Data mining #Data science #External Data Representation #FOS: Computer and information sciences #Feature (linguistics) #Feature selection #Geography #Machine Learning (cs.LG) #Machine learning #Missing data #Representation (politics) #Scale (ratio) #Selection (genetic algorithm) #Variety (cybernetics) #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.09606
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/10/18 · openalex publication_date 2021/10/18 · arxiv updated 2021/10/20 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05
Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before, by leveraging such big data. This data is of many different varieties, and can be of different levels of veracity e.g., precise, imprecise, uncertain, and missing, making it challenging to extract important information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -- often in real-time -- the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19, and for potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation, and then propose a model that first performs efficient feature selection from such representation. We apply this approach on two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policy makers to focus on only certain attributes for the purposes of studying this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.