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COVID-19 Disease Identification Using Hybrid Ensemble Machine Learning Approach


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1 University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, India
     

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Corona viral infected disease 2019 (Covid-19) has created a pandemic in year 2020 taking many lives and affecting millions of people. Due to lack of sufficient testing resources and healthcare systems, many countries and hospitals are not able to test this disease as the workload on the existing laboratories is increasing. In the proposed work, we have used hybrid ensemble machine learning models to predict this disease based on clinical variables and standard clinical laboratory tests. The main motive of the ensemble model is that combination of classifiers will classify the unseen data samples more accurately and chances for misclassification is very less as compared to the classification made by a single classifier. The performance comparison from various classification techniques is also done to show that hybrid ensemble classifier has outperformed decision tree and Support Vector Machine based classification algorithms.

Keywords

Hybrid Ensemble Learning, Decision Tree, Support Vector Machine
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  • COVID-19 Disease Identification Using Hybrid Ensemble Machine Learning Approach

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Authors

Jyotsna Yadav
University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, India
Richa Bhardwaj
University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, India

Abstract


Corona viral infected disease 2019 (Covid-19) has created a pandemic in year 2020 taking many lives and affecting millions of people. Due to lack of sufficient testing resources and healthcare systems, many countries and hospitals are not able to test this disease as the workload on the existing laboratories is increasing. In the proposed work, we have used hybrid ensemble machine learning models to predict this disease based on clinical variables and standard clinical laboratory tests. The main motive of the ensemble model is that combination of classifiers will classify the unseen data samples more accurately and chances for misclassification is very less as compared to the classification made by a single classifier. The performance comparison from various classification techniques is also done to show that hybrid ensemble classifier has outperformed decision tree and Support Vector Machine based classification algorithms.

Keywords


Hybrid Ensemble Learning, Decision Tree, Support Vector Machine

References