Dengue Fever prediction using Naïve Bayes algorithm

The main objective of this research is to develop a prototype Dengue Fever Disease Prediction System using data mining modeling techniques, Naïve Bayes. The research method used is to classify patient data into positive and negative classes based on the calculation of probability values. 9 variables were used and symbolized in the form X1 to X9. This variable represents the symptoms of Dengue Fever disease. The results showed that the Naive Bayes algorithm can be used to predict Dengue Fever. This can be proven from the results of the dataset test which was carried out three times. The results of the first trial were 70 data training and 30 data testing and obtained accuracy value is 90%. The results of the second trial were 50 data training and 50 data testing and obtained accuracy value is 88% and the third trial results were 40 data training 60 data testing obtained accuracy value is 88.3%.