Abstrait

Performance Analysis of Classifiers to Effieciently Predict Genetic Disorders Using Gene Data

R Preethi, G M SuriyaaKumar, N G Bhuvaneswari Amma, G Annapoorani

In this paper, we study the performance of various classifier models for predicting disease classes using genetic microarray data. We analyze the best from among the four classifier methods namely Naïve Bayes, J48, IB1 and IBk. Classification is a technique to predict the best classifier. Classification is used to classify the item according to the features of the item with respect to the predefined set of classes. Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. In this paper, we classify the dataset using classes and we found the J48 classifier performs better in accurately predicting the disease classes.