An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Md.Jakir Hossen, T. Ramanathan, Abdullah Al Mamun
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引用次数: 0

Abstract

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.
基于机器学习分类器的集合特征选择法预测 COVID-19 疾病
2019 年冠状病毒病(COVID-19)这种呼吸道疾病给每个国家的经济造成了严重破坏,感染并导致数百万人死亡。这种致命的疾病给全人类的生命造成了巨大损失,而且至今仍未研制出确切的治疗方法。因此,这种疾病的控制和治愈主要取决于通过早期检测来限制其传播率。冠状病毒感染的检测有助于对感染患者进行隔离和专门护理。本研究论文提出了一种新型数据挖掘系统,该系统结合了集合特征选择方法和机器学习分类器,可有效识别 COVID-19 感染。本文评估了不同的特征选择方法,包括卡方检验、递归特征消除(RFE)、遗传算法(GA)、粒子群优化(PSO)和随机森林,以确定它们在提高机器学习分类器的分类准确性方面的有效性。本研究工作中考虑的分类器包括决策树、奈夫贝叶斯、K-近邻(KNN)、多层感知器(MLP)和支持向量机(SVM)。测试使用了两个 COVID-19 数据集,提议的系统从中提取了支持该数据集的最佳特征。分析了基于集合特征选择方法的机器学习分类器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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