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

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


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