Performance Evaluation of classification algorithms on Lymph disease prediction
J. Junia Deborah, Latha Parthiban · 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2018
In the last decade, a large number of computer aided diagnosis (CAD) tools are developed for the identification of different algorithm. For the disease diagnosis, classification algorithms based on ML (ML) techniques are commonly used. The classification algorithm uses a supervised learning methodology to makes the system or computer program to learn from the given input data and then employ the learning knowledge to identify the upcoming observations. This paper intends to evaluate the different classification algorithms namely radial basis function (RBF), Naive Bayes (NB), J48 and Olex-GA on the identification of Lymph diseases. For the performance evaluation of different classifiers, a benchmark Lymph dataset is used interms of different performance measures. The obtained results proved that the RBF network attained better performance compared to NB, J48 and Olex-GA.