id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-323582-7y8pt72r	Ahamad, Martuza	A Machine Learning Model to Identify Early Stage Symptoms of SARS-Cov-2 Infected Patients	2020-06-20		.txt	text/plain	4446	249	55	We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. We extracted important features of basic information (age, gender), symptoms (fever, cough, muscle soreness), diagnostic results (lung infection, radiographic imaging), prior disease/symptom history (pneumonia, diarrhea, runny nose) and some trajectory information (isolation treatment status, travel history) that are directly or indirectly related to COVID-19 disease. In our study, we developed and tested a range of machine learning approaches and found the most significant clinical COVID-19 predictive features were (in descending order): lung infection, cough, pneumonia, runny nose, travel history, fever, isolation, age, muscle soreness, diarrhea, and gender.	./cache/cord-323582-7y8pt72r.txt	./txt/cord-323582-7y8pt72r.txt