Thyroid Disease Detection Using Supervised Machine Learning Techniques
Aryan Tiwari, Gaurav Pratap Singh, Shailja Singh, Tarun Jain
Abstract
Thyroid ailment is a common endocrine disease that affects millions of people around the world. For effective treatment, accurate diagnosis and prediction are essential. The efficiency of various machine learning models for predicting thyroid disease is investigated in this research study. Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes, Kmeans, and an ensemble model were among the models used. The results showed that the Logistic Regression model was 96% accurate, while the Decision Tree and Random Forest models were both 99% accurate. SVM had an 87% accuracy rate, kNN had a 95% accuracy rate, and the ensemble model had a 97% accuracy rate. The accuracy rates of Naive Bayes and K-Means were lower. The outcomes of this study emphasise the potential of machine learning algorithms, notably Logistic Regression, decision tree, random forest, and ensemble models, in accurately diagnosing and predicting thyroid disease.