Machine learning-based prediction of COVID-19 diagnosis based on symptoms

npj Digital Medicine | medRxiv | PDF

Yazeed Zoabi¹, Shira Deri-Rozov¹, Noam Shomron^1
1 Tel Aviv University

This framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

Model Predictors and Exact Variable Names (True = 1, False = 0)

• Age over 60 - Age_60
• Sex - Male (Male=1, Female=0)
• Cough - Cough
• Shortness of breath - Shortness_of_breath
• Fever - Fever
• Sore throat - Sore_throat
• Headache - Headache
• Contact with a confirmed individual - Contact_with_confirmed

Model Outcome

The probability of being diagnosed with a COVID-19 infection.

Use

1. Import lgbm_model_*.txt using LightGBM 2.3.1 on Python 3.6.

2. Predict using your data.

Files in this repository

• lgbm_model_all_features.txt - The predictor that uses all 8 features
• lgbm_model_balanced_features.txt - The predictor that uses only balanced symptoms
• hyperparameters.txt - The hyperparameters used by lightGBM
• data/corona_tested_individuals_ver_0083.english.csv.zip - The tested individuals dataset downloaded from https://data.gov.il/dataset/covid-19 on November 15, 2020 and translated into English
• data/corona_tested_individuals_ver_006.english.csv.zip - The tested individuals dataset downloaded from https://data.gov.il/dataset/covid-19 on May 4, 2020 and translated into English. This is the version we used for the analysis.