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An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big...

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Autores principales: Debjit, Kumar, Islam, Md Saiful, Rahman, Md. Abadur, Pinki, Farhana Tazmim, Nath, Rajan Dev, Al-Ahmadi, Saad, Hossain, Md. Shahadat, Mumenin, Khondoker Mirazul, Awal, Md. Abdul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139459/
https://www.ncbi.nlm.nih.gov/pubmed/35626179
http://dx.doi.org/10.3390/diagnostics12051023
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author Debjit, Kumar
Islam, Md Saiful
Rahman, Md. Abadur
Pinki, Farhana Tazmim
Nath, Rajan Dev
Al-Ahmadi, Saad
Hossain, Md. Shahadat
Mumenin, Khondoker Mirazul
Awal, Md. Abdul
author_facet Debjit, Kumar
Islam, Md Saiful
Rahman, Md. Abadur
Pinki, Farhana Tazmim
Nath, Rajan Dev
Al-Ahmadi, Saad
Hossain, Md. Shahadat
Mumenin, Khondoker Mirazul
Awal, Md. Abdul
author_sort Debjit, Kumar
collection PubMed
description A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub.
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spelling pubmed-91394592022-05-28 An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP Debjit, Kumar Islam, Md Saiful Rahman, Md. Abadur Pinki, Farhana Tazmim Nath, Rajan Dev Al-Ahmadi, Saad Hossain, Md. Shahadat Mumenin, Khondoker Mirazul Awal, Md. Abdul Diagnostics (Basel) Article A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub. MDPI 2022-04-19 /pmc/articles/PMC9139459/ /pubmed/35626179 http://dx.doi.org/10.3390/diagnostics12051023 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Debjit, Kumar
Islam, Md Saiful
Rahman, Md. Abadur
Pinki, Farhana Tazmim
Nath, Rajan Dev
Al-Ahmadi, Saad
Hossain, Md. Shahadat
Mumenin, Khondoker Mirazul
Awal, Md. Abdul
An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
title An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
title_full An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
title_fullStr An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
title_full_unstemmed An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
title_short An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
title_sort improved machine-learning approach for covid-19 prediction using harris hawks optimization and feature analysis using shap
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139459/
https://www.ncbi.nlm.nih.gov/pubmed/35626179
http://dx.doi.org/10.3390/diagnostics12051023
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