Performance Analysis of Machine Learning Algorithms for Thyroid Disease

Thyroid disease arises from an anomalous growth of thyroid tissue at the verge of the thyroid gland. Thyroid disorderliness normally ensues when this gland releases abnormal amounts of hormones where hypothyroidism (inactive thyroid gland) and hyperthyroidism (hyperactive thyroid gland) are the two main types of thyroid disorder. This study proposes the use of efficient classifiers by using machine learning algorithms in terms of accuracy and other performance evaluation metrics to detect and diagnose thyroid disease. This research presents an extensive analysis of different classifiers which are K-nearest neighbor (KNN), Naive Bayes, support vector machine, decision tree and logistic regression implemented with or without feature selection techniques. Thyroid data were taken from DHQ Teaching Hospital, Dera Ghazi Khan, Pakistan. Thyroid dataset was unique and different from other existing studies because it included three additional features which were pulse rate, body mass index and blood pressure. Experiment was based on three iterations; the first iteration of the experiment did not employ feature selection while the second and third were with L-1-, L-2-based feature selection technique. Evaluation and analysis of the experiment have been done which consisted of many factors such as accuracy, precision and receiver operating curve with area under curve. The result indicated that classifiers which involved L-1-based feature selection achieved an overall higher accuracy (Naive Bayes 100%, logistic regression 100% and KNN 97.84%) compared to without feature selection and L-2-based feature selection technique.

Automated summary

This study suggests using efficient classifiers with machine learning algorithms to detect and diagnose thyroid disease. The experiment showed that classifiers using L-1-based feature selection achieved higher accuracy compared to other techniques.