The Role of Machine Learning and Artificial Intelligence in Improving Health Outcomes in Africa During and After the Pandemic: What Are We Learning on the Attainment of Sustainable Development Goals?

Farai Mlambo (), Cyril Chironda (), Jaya George () and David Mhlanga ()
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Farai Mlambo: School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg
Cyril Chironda: School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg
Jaya George: Chemical Pathology, University of the Witwatersrand
David Mhlanga: College of Business and Economics, The University of Johannesburg

Chapter Chapter 7 in The Fourth Industrial Revolution in Africa, 2023, pp 117-149 from Springer

Abstract: Abstract The COVID-19 pandemic has placed a huge stress on an already overburdened health system in Africa. Diagnosis is based on the detection of a positive RT-PCR test which may be delayed during peaks. Rapid diagnosis and risk stratification of high-risk patients allow for the prioritization of resources for patient care. The study aims were to classify patients as COVID-19 positive or negative and to further classify patients as severe or not severe based on outcomes using machine learning on routine laboratory tests. Data were extracted for all individuals who had at least one SARS-CoV-2 PCR test done via the NHLS between the periods of 1 March 2020 and 7 July 2020. Exclusion criteria is as follows: those less than 18 years, and those with indeterminate PCR tests. Results for 15,437 patients (3301 positive and 12,136 negative) were used to fit 6 machine learning models, namely, the logistic regression (LR) (the base model), decision trees DT), random forest (RF), extreme gradient boosting XGB, convolutional neural network (CNN), and self-normalizing neural network (SNN). Model development was carried out by splitting the data into training and testing sets of a ratio of 70:30, together with tenfold cross-validation re-sampling technique. The performance of the models varied with diagnostic sensitivity ranging from 85.25% for SNN to 97.73% for the RF models. The area under the curve ranged from 70% for DT to 93% for LR, with accuracy ranging from 84% for LR to 88% for SNN. Machine Learning ML can be incorporated into the laboratory information system and offers promise for early identification and risk stratification of COVID-19 patients, particularly in areas of resource-poor settings such as sub-Saharan Africa. According to our findings, the efficient application of machine learning and artificial intelligence has the potential to assist African countries in accelerating their progress toward sustainable development goals, with a particular emphasis on goal 3.

Keywords: Africa; Artificial intelligence; Health outcomes; Machine learning pandemic sustainable development goals (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-28686-5_7

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