Comparative study of classification method on diagnosis of plasmodium phase

Malaria is a plague in humans induced by the Plasmodium parasitoid transferred through a single bite of female Anopheles mosquitoes. Once reaching human blood passage, this parasitoid bears asexual proliferation which is classified toward three phases (trophozoite, schizont, and gametocyte). To discover the phases, the paramedic will analyse the blood specimen of the subject within the microscope. However, the aforementioned approach has the potential to misdiagnosis. Many CAD-based investigations have been carried out to reduce the diagnosis errors that have formed. This research sought to develop a CAD-based design that can help paramedics in diagnosing the parasitoid Plasmodium, including trying to get a classification algorithm that is able to analyze the Plasmodium phase precisely. Based on the experiment outcome, Naïve Bayes was mostly useful applied as a classifier that attained the accuracy of 97.29%, the sensitivity, and specificity of 97.30% toward the P.vivax case. In the case of P.falciparum, it emerged in the accuracy, sensitivity, and specificity of 98.36%, 98.40%, and 98.40% apiece. Meantime, Perceptron was a useless algorithm applied as a classifier that realized the accuracy up to 81.08%, sensitivity and specificity of 93.80% on each. In the case of P.vivax, the accuracy, sensitivity, and specificity realized were 80.33%, 92.50%, and 92.50% respectively.