چکیده (انگلیسی):

Automated prediction of new patients’ disease diagnosis based on data mining analysis on historical data is
proven to be an extremely useful tool in the medical innovation. There are several studies focusing on this
particular aspect. The objective of this study is two-fold. First, we look into three different classifiers, which
are the Naïve Bayes, Multilayer Perceptron (MLP) and Decision Tree J48 to predict the diagnosis results.
Next, we investigate the effects of feature selection in such experiments. We also compare the experimental
results with the study of Comparative Disease Profile (CDP) using the same dataset. Results have shown
that the Naive Bayes provides the best result in terms of accuracy in our experiments and in comparison
with CDP. However, we suggest using Multilayer Perceptron since the variables used in our experiments
are inter-dependent among each other. In addition, MLP has shown better accuracy than CDP.