Thyroid Disease Prediction based on Feature Selection and Machine Learning

Abstract

Thyroid illness is a medical disorder in which the thyroid gland fails to produce enough hormones. Males, females, babies, teenagers, and the elderly are all susceptible to thyroid illness. It could be present from birth (hypothyroidism), or it could develop as you become older (often after menopause in women). People with thyroid diseases suffer from various problems like gaining weight, forgetfulness, anxiety, losing weight, fatigue, sleeping disorder, etc. So, diagnosing thyroid diseases is a vital issue as the diseases can be cured through proper and timely diagnosis. Recently machine learning techniques are used for diagnosing thyroid diseases. The feature selection approach was used to eliminate certain irrelevant characteristics from the thyroid dataset (from the UCI machine learning repository) and to select optimal features. The dataset has three target classes named normal, hypothyroid, and hyperthyroid. The subjects were classified through seven different machine-learning algorithms. Random Forest classifier achieves the highest accuracy 99.58% which is better than the existing state-of-the-art methods.