Effectiveness of Support Vector Machines in Medical Data mining

Published online: Mar 23, 2015 Full Text: PDF (955 KiB) DOI: 10.24138/jcomss.v11i1.114
Cite this paper
Padmavathi Janardhanan, Heena L., Fathima Sabika


The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. One of the positive aspects is to discover the important patterns. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. In this case, data mining prepares the ability of research and discovery that may not have been evident. This paper analyzes the effectiveness of SVM, the most popular classification techniques in classifying medical datasets. This paper analyses the performance of the Naïve Bayes classifier, RBF network and SVM Classifier. The performance of predictive model is analysed with different medical datasets in predicting diseases is recorded and compared. The datasets were of binary class and each dataset had different number of attributes. The datasets include heart datasets, cancer and diabetes datasets. It is observed that SVM classifier produces better percentage of accuracy in classification. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust and effective classifier for medical data sets.


Medical data mining, Navie Bayes, RBF, Support Vector Machines
Creative Commons License 4.0
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.