Classification of Naive Bayes Algorithm on Dengue Hemorrhagic Fever and Typhoid Fever Based on Hematology Results - Handayani, Yuni; Hakim, Alvin Rainaldy (Journal of Applied Intelligent System, 2023)
The application of increasing technology developed explicitly in the health field would significantly have an urgent role in guaranteeing quality service. Application deep data mining techniques classifier method, one among them used for classify something possibility, for example for classification disease. Dengue Hemorrhagic Fever is a disease caused by the dengue virus biting the Aedes aegypti mosquito. Meanwhile, Typhoid Fever is a disease caused by the bacterium Salmonella typhi. The second disease could attack all types of circles, fine children or mature ones. The second disease is almost the same symptom, so a proper diagnosis is needed to differentiate it. Study this applies the Naive Bayes algorithm to classify Dengue Hemorrhagic Fever and Typhoid Fever using 250 yield data test blood routine hematology at Tugurejo Hospital. Attributes used in the study, age, type sex, temperature, leukocytes, erythrocytes, hemoglobin, hematocrit, platelets, anti-dengue IgG, anti-dengue IgM, salmonella typhi o and salmonella Typhi h. The Naïve Bayes method is one of the techniques that can be used to perform analysis in determining the diagnostic results from a number of data studied with the aim of producing optimal results. The use of the Naïve Bayes method in this application is due to the probability that the accuracy value of the Nave Bayes method is close to the accuracy value of the experts.[12] The results of testing the Naive Bayes method using a confusion matrix show Recall value is 97.62%, Precision is 93.89%, accuracy is 93.33%, and Error Rate is 6 %. It can be concluded that this method is suitable for classifying Dengue Hemorrhagic Fever and Typhoid Fever and can be applied in studying this.