• Dr. Y. Angeline Christobel

In knowledge discovery process, classification is an important technique of data mining and widely used in various fields. The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. The aim of this paper is to investigate the performance of different classification methods for diabetic medical dataset. The performance of C4.5, Naïve Bayes, SVM and Multilayer Perceptron algorithms are evaluated based on Accuracy, Sensitivity, Specificity and Error rate.

Data Mining, Classification, C4.5, SVM,MLP, Naïve Bayes

"AN ANALYSISOF DATA MINING CLASSIFICATION METHODS FOR MEDICAL DATASET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.212-218, March-2019, Available :http://www.jetir.org/papers/JETIRAQ06044.pdf

"AN ANALYSISOF DATA MINING CLASSIFICATION METHODS FOR MEDICAL DATASET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp212-218, March-2019, Available at : http://www.jetir.org/papers/JETIRAQ06044.pdf