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‎There are different types of classification methods for classifying the certain data‎. ‎All the time the value of the variables is not certain and they may belong to the interval that is called uncertain data‎. ‎In recent years‎, ‎by assuming the distribution of the uncertain data is normal‎, ‎there are several estimation for the mean and variance of this distribution‎. ‎In this paper‎, ‎we consider the mean and variance for each of the start and end of intervals‎. ‎Thus we assume that the distribution of uncertain data is bivariate normal distribution‎. ‎We used the maximum likelihood to estimate the means and variances of the bivariate normal distribution‎. ‎Finally‎, ‎Based on the Naive Bayesian classification‎, ‎we propose a Bayesian mixture algorithm for classifying the certain and uncertain data‎. ‎The experimental results show that the proposed algorithm has high accuracy.

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Today, the diagnosis of diseases using artificial intelligence and machine learning algorithms are of great importance, because by using the data available in the study field of the desired disease, useful information and results can be obtained that reduce the occurrence of many deaths. Among these diseases, we can mention the diagnosis of diabetes, which has spread today due to the growth of urban life and the decrease in people's activity. So, it is very important to know whether a person is suffering from diabetes or not. In this article, the data set related to the information of people who have done the diabetes diagnosis test is used, this information is related to 520 people. People are classified into two groups based on whether their diabetes test result is positive or not, and Bayesian classification methods such as Bayesian Support Vector Machine, Naive Bayes, CNK and CatBoost ensemble classification method have been used to conclude which of these The methods can have a better ability to analyze the data and also to compare these methods use accuracy, precision, F1-score, recall, ROC diagram.