Diagnosis of fever symptoms using naive bayes algorithm

Abstract

Dengue Hemorrhagic Fever (DHF) and Typhus Fever (TF) are diseases that have similar symptoms. Fever in DHF is caused by the bite of the Aedes Aegypti mosquito, whereas fever in TF is caused by the bacterium Salmonella Typhi. The similarity of symptoms in these two diseases often leads to misdiagnosis of the patient, which can cause the patient's condition to worsen due to incorrect handling. To overcome this problem, we need a method to diagnose the symptoms of fever in both diseases. In data mining, the diagnosis of the disease can be done by classification techniques. The classification process for diagnosing fever symptoms is using the Naïve Bayes algorithm. Algorithm testing is done using k-fold cross-validation, with k equal to 10. The evaluation of the algorithm is measured by calculating the value of accuracy, precision, and recall from prediction results. The results showed that the average accuracy rate was 94%, precision was 90%, and recall was 92%. This shows that the Naïve Bayes algorithm has good performance in diagnosing fever in patients.