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. 2022 Sep 1;12(9):1367.
doi: 10.3390/life12091367.

A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic

Alqahtani Saeed  1 , Maryam Zaffar  2 , Mohammed Ali Abbas  2 , Khurrum Shehzad Quraishi  3 , Abdullah Shahrose  4 , Muhammad Irfan  5 , Mohammed Ayed Huneif  6 , Alqahtani Abdulwahab  6 , Sharifa Khalid Alduraibi  7 , Fahad Alshehri  7 , Alaa Khalid Alduraibi  7 , Ziyad Almushayti  7
Affiliations

A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic

Alqahtani Saeed et al. Life (Basel). .

Abstract

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19's impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students' health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student's remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

Keywords: COVID-19; artificial intelligence; feature selection; human health; lock down; mental stress; pandemic.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed flow AI-Based feature selection of factors affecting the health of students in COVID-19.
Figure 2
Figure 2
Variable count before sampling.
Figure 3
Figure 3
Variable count after sampling.
Figure 4
Figure 4
Some feature selection process of factors affecting the student’s health during COVID-19.
Figure 5
Figure 5
Comparison of accuracy of proposed COVID-19 approach.
Figure 6
Figure 6
Comparison of precision of proposed COVID-19 approach.
Figure 7
Figure 7
This Comparison of recall of proposed COVID-19 approach.
Figure 8
Figure 8
This Comparison of f-measure of proposed COVID-19 approach.
See this image and copyright information in PMC

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