A Machine Learning Approach for Diagnosing Meningococcal Meningitis
Keywords:
Meningococcal Meningitis, Bacteria Meningitis, Neisseria Meningitidis, Prediction, Machine Learning, Bayesian Belief Network ComponentAbstract
Meningococcal Meningitis is a hazardous sickness brought about by meningococcal microbes called Neisseria meningitidis which offers ascend to irritation of the meninges which influences people extending from babies, more established youngsters and grown-ups in particular old enough. The manifestations of this sickness are fatigue, nausea, seizures, vomiting, stiff neck, cold, sleepiness, skin rash, spasm, cough, loss of appetite, fever just to name a yet a couple. In late past, a few systems have been created to analyze this endemic malady; however they created a great deal of bogus negative during testing and couldn`t distinguish meningococcal meningitis, its overlapping symptoms and serogroup types. Thus, in this paper, we proposed and simulated a model to anticipate meningococcal meningitis and its serogroup types utilizing an AI strategy called Bayesian Belief Network. The model was structured utilizing Bayes Server and tried with data gathered from meningitis medical repository. The model had a 99.99% forecast precision, 97.12% sensitivity of Meningococcal Meningitis disease, 95.42% sensitivity of Serogroup type A, Serogroup type B, Serogroup type C, and Neisseria Meningitidis in that order
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