RULE INDUCTION ALGORITHM IN THE DIAGNOSIS AND CLASSIFICATION OF SOME COMMONLY RELATED SEXUALLY TRANSMITTED DISEASES

Odikwa, Ndubuisi Henry, Thom-Manuel, Osaki Miller, Uzoaru, Godson Chetachi
Keywords: Rule-Induction, Machine- Learning Algorithm, Staphylococcus, Candidiasis.
Clifford University International Journal of Development Studies (CLUIJODS) 2023 3(1), 18 - 31. Published: December 2023

Abstract

Many lives are lost today around the whole world as a result of improper diagnosis of diseases and more so confusable diseases that have commonly related symptoms. These diseases pose huge risks to human lives when it is not detected early or misdiagnosed for other diseases. Consequently, in the diagnosis of diseases using machine-learning algorithms, more of the algorithms are suitable for the diagnosis of diseases while some may not be appropriate. In this research paper, a more suitable machine-learning algorithm was proposed; which employs rules and inferences in the diagnosis and classification of diseases. The research paper employed rule induction algorithm for the diagnosis of commonly transmitted diseases of e-coli, staphylococcus, gonorrhea, syphilis and candidiasis based on 250 patient datasets collected from Federal Medical Center Owerri. The result obtained yielded a classification accuracy of 96% sensitivity of 96% and specificity of 71%.