An Enhanced Binary Multi-Objective Hybrid Filter-Wrapper Chimp Optimization based Feature Selection Method for COVID-19 Patient Health Prediction
IEEE Access
; : 1-1, 2022.
Article
in English
| Scopus | ID: covidwho-2051919
ABSTRACT
This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer’s search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts modifying the population using HHO to produce an HHO-based population;creating hybrid entities according to HHO-based and ChOA-based individuals;and altering the search agent in the light of greedy technique and ChOA’s tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients. Author
Biomedical monitoring; Chimp optimizer; COVID-19; Feature extraction; Feature selection; Filtering algorithms; Harris Hawk Optimizer; Medical data mining; Metaheuristics; Multi-objective optimization; Optimization; Patient monitoring; Predictive models; Bioinformatics; Data mining; Filtration; Forecasting; Medical applications; Multiobjective optimization; Features extraction; Features selection; Filtering algorithm; Metaheuristic; Multi-objectives optimization; Optimisations; Optimizers
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
IEEE Access
Year:
2022
Document Type:
Article
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