Abstract:
Nowadays, it has been noted that using the application of data mining techniques for predicting the outbreak of
the disease has been permitted in the health institutions which have relative opportunities for conducting the
treatment of diseases. The main target of this paper is to develop a hybrid based classification and regression
model for diseases outbreak prediction in datasets. In this view, the mixture of FT, Random Forest, Naïve Bayes
Multinomial, SMO, IB1, Simple Logistic and Bayesian Logistic Regression are applied to develop this hybrid
model. Accordingly, in hybrid model from this paper there is a core achievements of getting an enhancement as
the results described from experiments for combination of more than one Algorithms or methods classifier
models discovered that some Algorithms can boost or enhance others through hybrid so that they become more
strong significant basing on the accuracy of 100% as output results from hybrid training and with the accuracy
of 75% as output results from hybrid evaluation and based on other metrics measurement described on tables
4.1,4.2 and figures 4.1,4.2.
