Identification of Variable Importance for Predictions of Mortality From Covid-19 Using AI Models for Ontario, Canada
Bibliographic Data
| ID | 22075844 |
|---|---|
| Authors | Brett Snider (0000-0003-4883-6045, University of Guelph, corresponding author), Edward A Mcbean (0000-0002-4701-5467, University of Guelph), John Yawney (0000-0003-4269-9537, Alberta Oil Sands Technology and Research Authority), S Andrew Gadsden (0000-0003-3749-0878, University of Guelph), Bhumi Patel (University of Guelph) |
| Year | 2021 |
| Volume | 9 |
| Pages | 675766-675766 |
| Publication date | 2021-06-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.675766 |
| PMID | 34235131 |
| OpenAlex | W3174667040 |
| Language | EN |
| References cited | 18 |
The Severe Acute Respiratory Syndrome Coronavirus 2 pandemic has challenged medical systems to the brink of collapse around the globe. In this paper, logistic regression and three other artificial intelligence models (XGBoost, Artificial Neural Network and Random Forest) are described and used to predict mortality risk of individual patients. The database is based on census data for the designated area and co-morbidities obtained using data from the Ontario Health Data Platform. The dataset consisted of more than 280,000 COVID-19 cases in Ontario for a wide-range of age groups; 0–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and 90+. Findings resulting from using logistic regression, XGBoost, Artificial Neural Network and Random Forest, all demonstrate excellent discrimination (area under the curve for all models exceeded 0.948 with the best performance being 0.956 for an XGBoost model). Based on SHapley Additive exPlanations values, the importance of 24 variables are identified, and the findings indicated the highest importance variables are, in order of importance, age, date of test, sex, and presence/absence of chronic dementia. The findings from this study allow the identification of out-patients who are likely to deteriorate into severe cases, allowing medical professionals to make decisions on timely treatments. Furthermore, the methodology and results may be extended to other public health regions
Artificial neural network · Biology · Disease · Geography · Logistic regression · Machine learning · Pandemic · Pathology · Public health · Random forest · Statistics · Computer Science · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI · Demography · Machine Learning in Healthcare · Mathematics · Medicine · Artificial Intelligence · Internal Medicine
| Highly cited | No |
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