COVID-19 Prediction Using Supervised Machine Learning




Ali, Irfan

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Early diagnosis is important to stop the spread of illnesses that endanger human life. COVID-19 is a contagious disease that has mutated into multiple variants and created a global epidemic that requires immediate diagnosis. With the increase in COVID-19 cases, the amount of associated data grows every day, and data mining can be used to extract information from this data. In this project, a COVID-19 symptoms and presence dataset is used with several supervised machine learning algorithms to predict COVID-19 in the human body by examining the symptoms. The Bayes Net, Simple Logistic, Bagging, Support Vector Machine (SVM), and AdaBoost M1 classifiers are considered using the open-source Waikato Environment for Knowledge Analysis (WEKA) Machine Learning (ML) tool. Principal Component Analysis (PCA) is used to reduce the number of features in the dataset based on eigenvalues. Then the model is trained and tested using 5-fold cross-validation, 10- fold cross-validation, and 66/34 and 34/66 splits. The performance of the models is evaluated based on accuracy, precision, recall, F-measure, and execution time. The results obtained show that Bagging outperforms the other classifiers with an accuracy of 99.3% and an execution time of 0.10 s for a 66/34 split using 10 features.



Data Analytics, Machine Learning, COVID-19