Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method

Fri Oct 7 02:28:20 2022

(2022) A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. Informatics in Medicine Unlocked. p. 100825. ISSN 2352-9148 (Print) 2352-9148 (Linking)

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Official URL: https://www.ncbi.nlm.nih.gov/pubmed/34977330

Abstract

Background: Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. Methods: In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. Results: Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93) in predicting the risk of intubation among hospitalized COVID-19 patients. Conclusions: The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and to identify high risk patients.

Item Type: Article
Creators:
CreatorsEmail
Varzaneh, Z. A.UNSPECIFIED
Orooji, A.UNSPECIFIED
Erfannia, L.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Keywords: Artificial intelligent Covid-19 Coronavirus Data mining Intubation Machine learning Mechanical ventilator
Divisions:
Page Range: p. 100825
Journal or Publication Title: Informatics in Medicine Unlocked
Journal Index: Pubmed
Volume: 28
Identification Number: https://doi.org/10.1016/j.imu.2021.100825
ISSN: 2352-9148 (Print) 2352-9148 (Linking)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/3855

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