With the emergence of new COVID-19 variants, and infections continuing to rise, effective diagnostic software systems are urgently required to support the increasingly overstretched virus testing services. In addition, these software systems can help to stem the spread of COVID-19 by enabling early identification of new cases. This can be particularly important in low-income countries where medical personnel and facilities are limited.
Computational algorithms can also play a valuable role in identifying cases that traditional clinical diagnosis methods may miss; for example, infections in people with certain underlying diseases.
While artificial intelligence models are already available to support diagnosis of COVID-19, most are used in interpreting X-ray image data; and are not always effective in early-stage diagnosis, when the patient’s respiratory and cardiovascular systems may show few signs of the virus.
In a study published in the KeAi journal Data Science and Management , a team of researchers from Africa and Canada tested the ability of seven computational algorithms to diagnose COVID-19 at an early stage, based on the following common symptoms: fever or chills; cough; shortness of breath or difficulty breathing; fatigue; muscle or body aches; headache; loss of taste or smell; sore throat; congestion or runny nose; nausea or vomiting; and diarrhoea .
The researchers found that the algorithms Multilayer Perceptron, Fuzzy Cognitive Map and Deep Neural Network outperformed Logistic Regression, Naïve Bayes, Decision Tree and Support Vector Machine.
Corresponding author Boluwaji A. Akinnuwesi, an Associate Professor in the Department of Computer Science at University of Eswatini in southern Africa, believes these findings could guide future software development. “This information could be adopted to develop intelligence-based software that both medical personnel and patients can use for early diagnosis of COVID-19 when these symptoms are present. At the time we were conducting this research, we could not find any other studies that had applied any of the listed intelligent techniques for COVID-19 diagnosis using these common symptoms.”
He adds: “Using these algorithms is a better option than exposing patients to X-rays, which, in addition, are not always easily accessible. The three best performing algorithms have the potential to be developed into widely-available software, increasing access to quick and affordable diagnosis of COVID-19 infection, which is particularly important for low-income countries, like Africa.”
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Contact the corresponding author: Boluwaji A. Akinnuwes, moboluwaji@gmail.com
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 100 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
Data Science and Management
Computational simulation/modeling
People
Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.