A Comparative Study for Various Methods of Classification

This paper discusses data mining techniques to process a medical dataset and identify the relevance of liver disorder and drinking alcohol drink by classification of blood test data. We have used four different classification methods including decision tree, Bayesian algorithms (Naive Bayes and Bayesian Networks), Neural Network classification and Rough Sets methods. To evaluate the methods, we have used the Waikato Environment for Knowledge Analysis (WEKA) open source tool. WEKA is a collection of machine learning algorithms that can be used for different processing tasks such as classification, and clustering. Bayesian algorithms and Neural Network classification methods are implemented with WEKA. However, as WEKA does not support methods based on Rough Sets we have used Rosetta. We have provided an evaluation based on applying these classification methods to our dataset and measuring the accuracy of test results. The evaluation results show that using Neural Networks obtains the best result among the other methods.