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

Diagnostics

Farhana Tazmim Pinki¹  Saad Al-Ahmadi²  Md Saiful Islam²  Md. Shahadat Hossain³  Khondoker Mirazul Mumenin⁴  Md. Abdul Awal⁴  Rajan Dev Nath⁵  Kumar Debjit⁶  Md. Abadur Rahman⁷ 

[1] Computer Science and Engineering Discipline (CSE), Khulna University (KU), Khulna 9208, Bangladesh;Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;Department of Quantitative Sciences, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh;Electronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, Bangladesh;Faculty of Business, Education, Law and Arts, School of Commerce, University of Southern Queensland, Darling Heights, QLD 4350, Australia;Faculty of Health, Engineering and Sciences, University of Southern Queensland, 487-535 West Street, Toowoomba, QLD 4350, Australia;Faculty of Science and Engineering, Southern Cross University, East Lismore, NSW 2480, Australia;

关键词: big COVID-19 data;    HHO;    machine learning;    decision support system;    healthcare;   

DOI  :  10.3390/diagnostics12051023

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown