Non-communicable chronic diseases such as cardiovascular diseases and diabetes and the risk factors of these diseases are becoming an increasing health and development problem in the world. Non-communicable chronic diseases are among the most important causes of death according to the World Health Organization (WHO). The prediction of death or survival is very important in terms of contributing to scientific studies for the earlier diagnosis of non-communicable chronic diseases. Today's developing world, where technology and artificial intelligence can be used in every field, enables the prediction of survival in chronic diseases to be realized with many machine learning methods. In order to know which artificial intelligence or machine learning method is the most effective, it will be very useful to make applications with the methods used and even with the subclasses of the same method and to compare the classification results obtained from the applications with each other. In this study, survival in chronic diseases was estimated by using decision tree methods in four different structures designed by training with body mass index taken from individuals with chronic diseases and other hospital records. The highest accuracy rate was obtained with the optimizable decision trees (ODT) method, which is the simplest model among these models, which allows the most optimal selection of hyperparameters.