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Journal of Cybersecurity and Information Management
Volume 8 , Issue 1, PP: 08-16 , 2021 | Cite this article as | XML | Html |PDF

Title

Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction

  Mohamed Abdel-Basset 1 * ,   Mohamed Elhoseny 2

1  Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt
    (analyst_mohamed@zu.edu.eg)

2  Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
    (Mohamed_elhoseny@mans.edu.eg)

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Doi   :   https://doi.org/10.54216/JCIM.080101

Received: April 11, 2021 Accepted: August 21, 2021

Abstract :

In the current epidemic situations, people are facing several mental disorders related to Depression, Anxiety, and Stress (DAS). Numerous scales are developed for computing the levels for DAS, and DAS-21 is one among them. At the same time, machine learning (ML) models are applied widely to resolve the classification problem efficiently, and feature selection (FS) approaches can be designed to improve the classifier results. In this aspect, this paper develops an intelligent feature selection with ML-based risk management (IFSML-RM) for DAS prediction. The IFSML-RM technique follows a two-stage process: quantum elephant herd optimization-based FS (QEHO-FS) and decision tree (DT) based classification. The QEHO algorithm utilizes the input data to select a valuable subset of features at the primary level. Then, the chosen features are fed into the DT classifier to determine the existence or non-existence of DAS. A detailed experimentation process is carried out on the benchmark dataset, and the experimental results showcased the betterment of the IFSML-RM technique in terms of different performance measures. 

Keywords :

Risk management , Machine learning , Feature selection , DAS , Prediction model , Classification.

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Cite this Article as :
Style #
MLA Mohamed Abdel-Basset, Mohamed Elhoseny. "Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction." Journal of Cybersecurity and Information Management, Vol. 8, No. 1, 2021 ,PP. 08-16 (Doi   :  https://doi.org/10.54216/JCIM.080101)
APA Mohamed Abdel-Basset, Mohamed Elhoseny. (2021). Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction. Journal of Journal of Cybersecurity and Information Management, 8 ( 1 ), 08-16 (Doi   :  https://doi.org/10.54216/JCIM.080101)
Chicago Mohamed Abdel-Basset, Mohamed Elhoseny. "Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction." Journal of Journal of Cybersecurity and Information Management, 8 no. 1 (2021): 08-16 (Doi   :  https://doi.org/10.54216/JCIM.080101)
Harvard Mohamed Abdel-Basset, Mohamed Elhoseny. (2021). Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction. Journal of Journal of Cybersecurity and Information Management, 8 ( 1 ), 08-16 (Doi   :  https://doi.org/10.54216/JCIM.080101)
Vancouver Mohamed Abdel-Basset, Mohamed Elhoseny. Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction. Journal of Journal of Cybersecurity and Information Management, (2021); 8 ( 1 ): 08-16 (Doi   :  https://doi.org/10.54216/JCIM.080101)
IEEE Mohamed Abdel-Basset, Mohamed Elhoseny, Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction, Journal of Journal of Cybersecurity and Information Management, Vol. 8 , No. 1 , (2021) : 08-16 (Doi   :  https://doi.org/10.54216/JCIM.080101)
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