A Supervised Machine Learning Model for Early Detection of Epilepsy and Seizure Disorders Based On Observed Side-Effects

Epilepsy is a neurological disorder that is persistent and
characterized by uncontrolled seizure which influences
individuals in respective of age and sex, with the human brain
being the major spot where it is instigated without any evidence
of the cause of trigger, hence affecting any part of the body.
Owing to its paroxysmal nature which has affected more than 50
million individuals globally, the World Health Organization
categorized epilepsy as a major and most universal neurological
disease worldwide with 80% of these infected individuals living
in low and middle-income countries of Sub-Sahara Africa. Even
so, in the recent past, several systems have been developed to
detect this non-communicable ailment, yet they delivered a ton of
bogus negative during testing and couldn't distinguish epilepsy
because of the overlapping symptoms it imparts to other seizure
disorders. Hence, in this paper, we proposed and built up a model
to predict epilepsy and seizure disorders using an AI technique
called Bayesian Belief Network. The model was structured using
Bayes-Server and tested with data retrieved from the epilepsy
machine learning repository. The model had an overall prediction
exactness of 99.98%; 99.65% and 99.45% sensitivity of epilepsy
and seizure disorders in that order.