Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms

Zhanyang Sun, Rui Ding, Xinyu Zhou

Keywords: COVID-19, Machine learning, Classification

Abstract:

Predicting the COVID-19 outbreak has been studied by many researchers in recent years. Many
machine learning models have been used for the prediction of the transmission in a country or
region, but few studies aim to predict whether an individual has been infected by COVID-19.
However, due to the gravity of this global pandemic, prediction at an individual level is critical.
The objective of this paper is to predict if an individual has COVID-19 based on the symptoms
and features. The prediction results can help the government better allocate the medical resources
during this pandemic. Data of this study was taken on June 18th from the Israeli Ministry of
Health on COVID-19. The purpose of this study is to compare and analyze different models,
which are Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayesian (NB),
Decision Tree (DT), Random Forest (RF) and Neural Network (NN).

One-sentence Summary: This paper compares and analyzes different models which aim to predict if an individual has COVID-19 based on the symptoms and features.