The Coronavirus disease 19 (COVID-19) is an ongoing global pandemic where it is easily transmittable and life threatening the world. The number of infected and non-survived patients is increasing in almost all the affected countries. Currently, there is no clinically approved vaccine available yet. Early prediction is necessary to assist the healthcare systems to strategize and reduce the spread of this virus. This is a very critical decision that is considered as a potential threat to others. Supervised Machine Learning (SML) models have demonstrated promising performance in various prediction applications that can improve decision making. Thus, this research investigates the capabilities of SML models to predict whether a patient is infected with COVID-19 or not based on certain symptoms. A comparative analysis of the impact of seven standard SML prediction models has been conducted. They are Adaboost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Neural Network, Random Forest, and Support Vector Machine. A publicly available dataset from kaggle.com has been utilized for this research that consists of twenty symptoms collected from eight different countries. The outcome from Random Forest revealed that the five most important symptoms are tiredness, fever, dry cough, nasal congestion and those whose age is more than 60. These symptoms are consistent for all eight countries. Besides that, experimental results of the SML models also indicate that Neural Network achieves the best predictive results followed by Adaboost.
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In-Text Citation: (Ibrahim et al., 2021)
To Cite this Article: Ibrahim, Z., Diah, N. M., Rizal, N. A., & Yuri, M. N. (2021). Prediction of Early Symptoms of COVID-19 Infected Patients using Supervised Machine Learning Models. International Journal of Academic Research in Business and Social Sciences, 11(12), 2633–2643.
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