Symptom-based Machine Learning Models for the Early Detection of
  COVID-19: A Narrative Review
Moyosolu Akinloye
Despite the widespread testing protocols for COVID-19, there are still
significant challenges in early detection of the disease, which is crucial for
preventing its spread and optimizing patient outcomes. Owing to the limited
testing capacity in resource-strapped settings and the limitations of the
available traditional methods of testing, it has been established that a fast
and efficient strategy is important to fully stop the virus. Machine learning
models can analyze large datasets, incorporating patient-reported symptoms,
clinical data, and medical imaging. Symptom-based detection methods have been
developed to predict COVID-19, and they have shown promising results. In this
paper, we provide an overview of the landscape of symptoms-only machine
learning models for predicting COVID-19, including their performance and
limitations. The review will also examine the performance of symptom-based
models when compared to image-based models. Because different studies used
varying datasets, methodologies, and performance metrics. Selecting the model
that performs best relies on the context and objectives of the research.
However, based on the results, we observed that ensemble classifier performed
exceptionally well in predicting the occurrence of COVID-19 based on patient
symptoms with the highest overall accuracy of 97.88%. Gradient Boosting
Algorithm achieved an AUC (Area Under the Curve) of 0.90 and identified key
features contributing to the decision-making process. Image-based models, as
observed in the analyzed studies, have consistently demonstrated higher
accuracy than symptom-based models, often reaching impressive levels ranging
from 96.09% to as high as 99%.