Generalizable prediction of COVID-19 mortality on worldwide patient data.

ABSTRACT:

Objective

Predicting Coronavirus disease 2019 (COVID-19) mortality for patients is critical for early-stage care and intervention. Existing studies mainly built models on datasets with limited geographical range or size. In this study, we developed COVID-19 mortality prediction models on worldwide, large-scale "sparse" data and on a "dense" subset of the data.

Materials and methods

We evaluated 6 classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), AdaBoost (AB), and Naive Bayes (NB). We also conducted temporal analysis and calibrated our models using Isotonic Regression.

Results

The results showed that AB outperformed the other classifiers for the sparse dataset, while LR provided the highest-

SUBMITTER: Edelson M 

PROVIDER: S-EPMC9129227 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature