Predicting Covid-19 infection using machine learning and feature selection methods

The purpose of this thesis is to develop new COVID-19 prediction models using different machine learning and feature selection methods. Particularly, Multi-Layer Perceptron (MLP), Tree Boost (TB), Radial Basis Function Network (RBF), Support Vector Machine (SVM), and K-Means Clustering (KMC) have been used to construct various regular and feature selection-based COVID-19 prediction models. The minimum redundancy maximum relevance (mRMR) and Relief-F algorithms have been chosen as the feature selectors. The dataset has information related to 20.000 patients (i.e., 10.000 positives, 10.000 negatives) and includes several personal, symptomatic, and non-symptomatic variables. The accuracy, precision, recall, and F1-score metrics have been used to assess the models' performance, whereas the generalization errors of the models has been evaluated using 10-fold cross-validation. The results show that, in general, MLP outperforms all other ML classifiers for predicting the COVID-19 infection. The average performance of mRMR is slightly better than Relief-F in predicting the COVID-19 infection of a patient. The symptom-based variables such as fever, cough, and headache have been found as the most vital predictors of COVID-19 infection.