دورية أكاديمية
Nature inspired optimization model for classification and severity prediction in COVID-19 clinical dataset.
| العنوان: | Nature inspired optimization model for classification and severity prediction in COVID-19 clinical dataset. |
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| المؤلفون: | Suma LS; Department of Computational Biology and Bioinformatics, University of Kerala, Trivandrum, India., Anand HS; Department of Computer Science and Engineering, Muthoot Institute of Technology and Science, Kochi, India., Vinod Chandra SS; Department of Computer Science, University of Kerala, Trivandrum, India. |
| المصدر: | Journal of ambient intelligence and humanized computing [J Ambient Intell Humaniz Comput] 2023; Vol. 14 (3), pp. 1699-1711. Date of Electronic Publication: 2021 Jul 31. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Country of Publication: Germany NLM ID: 101538212 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1868-5137 (Print) NLM ISO Abbreviation: J Ambient Intell Humaniz Comput Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Berlin : Springer |
| مستخلص: | The spread rate of COVID-19 is expected to be high in the wake of the virus's mutated strain found recently in a few countries. Fast diagnosis of the disease and knowing its severity are the two significant concerns of all physicians. Even though positive or negative diagnosis can be obtained through the RT-PCR test, an automatic model that predicts severity and the diagnosis will help medical practitioners to a great extend for affirming medication. Machine learning is an efficient tool that can process vast volume of data deposited in various formats, including clinical symptoms. In this work, we have developed machine learning models for analysing a clinical data set comprising 65000 records of patients, consisting of 26 features. An optimum set of features was derived from this data set by the proposed variant of artificial bee colony optimization algorithm. By making use of these features, a binary classifier is modelled with support vector machine for the screening of COVID-19 patients. Different models were tested for this purpose and the support vector machine has showcased the highest accuracy of 96%. Successively, severity prediction in COVID positive patients was also performed successfully by the logistic regression model. The model managed to predict three severity status viz mild, moderate, and severe. The confusion matrix and the precision-recall values (0.96 and 0.97) of the binary classifier indicate the classifier's efficiency in predicting positive cases correctly. The receiver operating curve generated for the severity predicting model shows the highest accuracy, 96.0% for class 1 and 85.0% for class 2 patients. Doctors can infer these results to finalize the type of treatment/care/facilities that need to be given to the patients from time to time. Competing Interests: Conflict of interestAuthors hereby confirm that there is no conflict of interest or competing interest for the article and work submitted in this journal. All Ethical norms are being followed during each and every phase of the work from the inception to the submission of the manuscript. (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.) |
| فهرسة مساهمة: | Keywords: Artificial bee colony optimization; COVID-19; Logistic regression; Severity prediction; Support vector machine |
| تواريخ الأحداث: | Date Created: 20210809 Latest Revision: 20230306 |
| رمز التحديث: | 20230306 |
| مُعرف محوري في PubMed: | PMC8325049 |
| DOI: | 10.1007/s12652-021-03389-1 |
| PMID: | 34367354 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1868-5137 |
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| DOI: | 10.1007/s12652-021-03389-1 |