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

Kumar Debjit; Md Saiful Islam; Md Abadur Rahman; Farhana Tazmim Pinki; Rajan Dev Nath; Saad Al-Ahmadi; Md Shahadat Hossain; Khondoker Mirazul Mumenin; Md Abdul Awal

doi:10.3390/diagnostics12051023

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

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

Kumar Debjit, Md Saiful Islam, Md Abadur Rahman, Farhana Tazmim Pinki, Rajan Dev Nath, Saad Al-Ahmadi, Md Shahadat Hossain, Khondoker Mirazul Mumenin and Md Abdul Awal

Diagnostics (Basel), Vol.12(5), pp.1-13

19/04/2022

DOI: https://doi.org/10.3390/diagnostics12051023

PMID: 35626179

Appears in Recent Faculty of Science and Engineering Publications

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Abstract

machine learning

big COVID-19 data

HHO

decision support system

healthcare

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.

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Title: An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP
Creators: Kumar Debjit - University of Southern Queensland
Md Saiful Islam - King Saud University
Md Abadur Rahman - Southern Cross University
Farhana Tazmim Pinki - Khulna University
Rajan Dev Nath - University of Southern Queensland
Saad Al-Ahmadi - King Saud University
Md Shahadat Hossain - Department of Quantitative Sciences, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh
Khondoker Mirazul Mumenin - Khulna University
Md Abdul Awal - Khulna University
Publication Details: Diagnostics (Basel), Vol.12(5), pp.1-13
Publisher: MDPI AG
Grant note: RG-1441-394 / King Saud University
Identifiers: 991013054708802368
Copyright: © 2022 by the authors. 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/).
Academic Unit: Faculty of Science and Engineering
Language: English
Resource Type: Journal article

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

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