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A Novel Approach of SVM based Classification on Thyroid Disease Stage Detection

Thyroid gland creates an important hormone for human body to function in a healthier way. IF thyroid hormone is produced extensively or too little below the threshold, then the condition is called thyroid disease. An accurate solution for detecting thyroid disease is the significant requirement in medical domain. The different types of thyroid disease have greater impact on human body by enabling weight loss, weight gain, anxiety, irritability, vision problems, intolerance to cold or hot temperature, etc. In this proposed work, multiclass support vector machine classifiers are trained using dataset from UCI repository to create a model and classify the thyroid into four types like Hypothyroidism, Hyperthyroidism, Euthyroid Sick, and Euthyroid. From the results of the proposed model, SVM is observed as a well-known approach in the machine learning community for binary classification problems. SVM offers a great accuracy with high dimensional space.