Prediction Of Thyroid Disorders Using Advanced Machine Learning Techniques

The paper presents several methods of feature selection and classification for thyroid disease diagnosis, related to the machine learning classification problems. Two common diseases of the thyroid gland, which releases thyroid hormones for regulating the rate of body's metabolism, are hyperthyroidism and hypothyroidism. Classification of these thyroid diseases is a considerable task. An important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. The proposed methods of feature selection are Univariate Selection, Recursive Feature Elimination and Tree Based Feature Selection. Three classification techniques have been used namely Naïve Bayes, Support vector machines and Random Forest. Results shows that the Support Vector Machines are the most accurate technique and hence this was used as a classifier to separate the symptoms of thyroid diseases into 4 classes namely Hypothyroid, Hyperthyroid, Sick Euthyroid and Euthyroid (negative).