Efficient Thyroid Disease Prediction using Features Selection and Meta-Classifiers

Prediction of Thyroid is a complex axiom in medical research. Machine learning methods are more powerful and compact for the healthcare industry to handle the massive amount of healthcare records. The methods in machine learning provides the facility to use different kind of data values which are used for prediction. Data cleaning techniques are used for enhancing the dataset to provide accurate results. The noisy and missing values are handled using data pre-processing methods. In this work, Adaboost and Bagging techniques are used for thyroid classification. The methods are executed, and the results are compared to show the effective method for thyroid prediction.