Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa

Emmanuel Chimbunde; Lovemore N Sigwadhi; Jacques L Tamuzi; Elphas L Okango; Olawande Daramola; Veranyuy D Ngah; Peter S Nyasulu

doi:10.3389/frai.2023.1171256

Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa

Front Artif Intell. 2023 Oct 10:6:1171256. doi: 10.3389/frai.2023.1171256. eCollection 2023.

Authors

Emmanuel Chimbunde¹, Lovemore N Sigwadhi¹, Jacques L Tamuzi¹, Elphas L Okango², Olawande Daramola³, Veranyuy D Ngah¹, Peter S Nyasulu^{1

4}

Affiliations

¹ Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
² African Health Research Institute, Durban, South Africa.
³ Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa.
⁴ Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

Abstract

Background: COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms.

Methods: Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics.

Results: From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO₂, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC.

Conclusion: Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.

Keywords: COVID-19; K-means clustering; artificial neural network; machine learning; multilayer perceptron.

Grants and funding

D43 TW010547/TW/FIC NIH HHS/United States