Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm

The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm

The COVID‐19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID‐19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi‐threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG‐ELM) to predict the severity level of the COVID‐19 patients. We conduct a set of experiments on a recently published real‐world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi‐threaded implementation with statistical analysis. In order to verify the efficiency of MG‐ELM, we compare our results with traditional and state‐of‐the‐art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A robust multiobjective Harris’ Hawks Optimization algorithm for the binary classification problem

The Harris’ Hawks Optimization (HHO) is a recent metaheuristic inspired by the cooperative behavior of the hawks. These avians apply many intelligent techniques like surprise pounce (seven kills) while they are catching their prey according to the escaping patterns of the target. The HHO simulates these hunting patterns of the hawks to obtain the best/optimal solutions to the problems. In this study, we propose a new multiobjective HHO algorithm for the solution of the well-known binary classification problem. In this multiobjective problem, we reduce the number of selected features and try to keep the accuracy prediction as maximum as possible at the same time. We propose new discrete exploration (perching) and exploitation (besiege) operators for the hunting patterns of the hawks. We calculate the prediction accuracy of the selected features with four machine learning techniques, namely, Logistic Regression, Support Vector Machines, Extreme Learning Machines, and Decision Trees. To verify the performance of the proposed algorithm, we conduct comprehensive experiments on many benchmark datasets retrieved from the University of California, Irvine (UCI) Machine Learning Repository. Moreover, we apply it to a recent real-world dataset, i.e., a Coronavirus disease (COVID-19) dataset. Significant improvements are observed during the comparisons with state-of-the-art metaheuristic algorithms.