Classification of Thyroid Disease with Feature Selection Technique

In medical science, Thyroid classification is one the important role for classification of thyroid diseases. Diagnosis of health condition is very challenging task for every human being because life is directly related to health condition. Data mining based classification is one of the important applications for classification of data. In this research work, we have used various classification techniques for classification of thyroid data. CART gives highest accuracy 99.47% as best model. Feature selection plays very important role to computationally efficient and increase the performance of model. This research work focus on Info Gain and Gain Ratio feature selection technique to reduce the irrelevant features from original data set and computationally increase the performance of model. We have applied both the feature selection techniques on best model i. e. CART. Our proposed CART-Info Gain and CARTGain Ratio gives 99.47% and 99.20% accuracy with 25 and 3 feature respectively.