EXAMINING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE AND DEEP LEARNING MODELS FOR THE PREDICTION OF COVID-19
Hafiza Zoya Mojahid1*, Jasni Mohamad Zain2, Marina Yusoff3, Abdul Basit4, Abdul Kadir Jumaat5 and Mushtaq Ali6
1*,2,3,4,5College of Computing, Informatics and Mathematics, University Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
2,3,5Institute for Big Data Analytics and Artificial Intelligence(IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
6Riphah International University, 7th Avenue, Sector G- 7/4, 44000 Islamabad, Pakistan
1*This email address is being protected from spambots. You need JavaScript enabled to view it., 2This email address is being protected from spambots. You need JavaScript enabled to view it., 3This email address is being protected from spambots. You need JavaScript enabled to view it., 4This email address is being protected from spambots. You need JavaScript enabled to view it., 5This email address is being protected from spambots. You need JavaScript enabled to view it., 6This email address is being protected from spambots. You need JavaScript enabled to view it.
ABSTRACT
Feature selection is a vital preprocessing step for identifying the most informative features in complex datasets, enhancing the efficiency and accuracy of machine learning models. Its applications extend across various domains, including big data analytics, finance, chemometrics, medical diagnostics, biological research, intrusion detection systems, and renewable energy solutions. In medical contexts, feature selection serves a dual purpose: it reduces dimensionality while simultaneously improving the comprehension of disease etiology. This study delves into key variable selection methods—specifically Recursive Feature Elimination (RFE), Principal Component Analysis (PCA) and Least Absolute Shrinkage and Selection Operator (LASSO). We evaluate the interaction of these methods with Support Vector Machines (SVM), Logistic Regression (LR), and eXtreme Gradient Boosting (XGBoost) for COVID-19 prediction. Key performance metrics, including F1-score, precision, recall, and accuracy. LASSO with SVM performed the best overall in terms of accuracy = 0.7679 and precision=0.8236, but PCA outperformed RFE with XGBoost, underscoring the importance of matching feature selection methods to model types. In addition, we employ a deep learning Feature Selection method based on Extreme Learning Machine (FSELM) and compare its effectiveness against the established feature selection techniques. Our work reveals that Lactate Dehydrogenase (LDH) is the most relevant feature while predicting COVID-19. This research aims to provide insights into the optimal integration of feature selection techniques with advanced machine learning models for accurate prediction of COVID-19 virus.
Keywords: COVID-19, Deep Learning, Extreme Learning Machine, Feature Selection, Machine Learning Models, Prediction.
Published On: 1 April 2025
