Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

作     者：Amani Yahyaoui Jawad Rasheed Shtwai Alsubai Raed M.Shubair Abdullah Alqahtani Buket Isler Rana Zeeshan Haider 

作者机构：Department of Software EngineeringIstanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software EngineeringNisantasi UniversityIstanbul34398Turkey Department of Computer ScienceCollege of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Electrical and Computer EngineeringNew York University(NYU)Abu Dhabi129188United Arab Emirates College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer EngineeringIstanbul Topkapi UniversityIstanbul34087Turkey Baqai Institute of HematologyBaqai Medical UniversityKarachi75340Pakistan 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第8期

页      面：2247-2261页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Artificial intelligence COVID-19 deep learning diagnosis machine learning 

摘      要：The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole *** then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this *** researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the *** paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose *** different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL *** approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,*** data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive *** dataset includes 18 attributes that characterize *** used accuracy,f1-score,recall and precision to evaluate the different developed *** obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based *** all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,*** these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%*** LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.