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Accurate diagnose of diseases prior to their treatment is a challenging task for the modern research, therefore it becomes necessary and important to use modern computing techniques to design an efficient and accurate prediction systems. Thyroid is one of the most common diseases found in human body with many side effects the accuracy for thyroid diagnosis system may be greatly improved by considering an ensemble algorithm technique. In this paper, an effective and accurate thyroid disease prediction model is developed using an ensemble of Bagging with J45 and ensemble of Bagging with SimpleCart to extract useful information and diagnose diseases. The performances of the two ensemble model were compared with single classifiers. The Bagging ensemble algorithm for thyroid prediction system promises excellent overall accuracy of 99.66% while other single selected classifiers like Bagging and SimpleCART has accuracy of 99.55% and J48 with accuracy of 99.60%. General Terms Machine Learning.

Effective and Accurate Bootstrap Aggregating (Bagging) Ensemble Algorithm Model for Prediction and Classification of Hypothyroid Disease