Supervised Machine Learning Model For Effective Classification Of Patients With Covid-19 Symptoms Based On Bayesian Belief Network
Keywords:
Bio-inspired computing, Computer aided diagnosis, Bayesian Network, Machine learning, Covid-19Abstract
Covid-19 is a contagious infection and should
be managed properly. If it is identified early
enough, the chances of survival are high.
Symptoms presentations may be confusing to
health practitioners since they are similar to
other diseases and the number of health
professionals are mostly insufficient,
especially in developing countries, hence a
correct and timely diagnoses based on
symptoms’ presentations can prove difficult.
So far, efforts to take address these needs are
still not adequate. In this paper, we leverage on
the efficacy of machine learning algorithms to
present a machine learning approach,
Bayesian Belief Network, that aims at ensuring
that the symptoms’ set are correctly and
timeously classified as either Covid-19+ or
Covid-19- .
A dataset is gathered from observed symptoms
of confirmed Covid-19 patients by healthcare
practitioners. The dataset is trained so as to be
able to classify, based on this prior knowledge,
any supplied symptom set. This has been able to
effectively handle the problem of wrong or
delayed diagnoses with its attendant negative
consequences. Our model yields 98% accuracy,
showing a significant classification accuracy of
patients with Covid-19 symptoms.
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