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Design of a Classifier System using Machine Learning Techniques for Diffuse Goiter Diagnosis
Abstract
A Diffuse goiter is usually an auto immune disease where the thyroid gland situated in the neck becomes enlarged. It is one of the most common thyroid disorders. In this paper, a diagnostic classifier system is designed to diagnose diffuse goiter using Thyroid Ultrasound (TUS) images. Normal and goiter TUS images are pre-processed using Matlab software to extract Region of Interest (ROI). Relevant features were extracted from ROI. These features are validated by classifier system using different machine learning techniques- Naïve Bayes, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Instance Based K-nearest neighbor (IBK) and J-48. The performance evaluation of the developed system is estimated using classification accuracy, time taken to build the model, Mean Absolute Error (MAE), Kappa Statistic and ROC Area. The obtained classification accuracies of different classifiers are in the range 88%-93%, 85%-91% and 83%-90% using 3-fold, 10-fold and hold-out (60%-40%) methods respectively. The results are very promising, indicating selected features were robust for diagnosis of diffuse goiter and also demonstrating the utility of machine learning techniques in medical domain.
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References
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