Accurate Thyroid Disease Detection with Ensemble Learning Models

Thyroid disease is a medical condition that prevents a person's thyroid gland from producing enough hormones and significantly affects a person's health. Early detection of thyroid disease and timely treatment can play an essential role in improving the health of thyroid patients. Our research paper helps to detect this disorder called thyroid disease and suggests an automated system as a complement to conventional treatment for healthcare. We conduct a wide range of studies, including exploratory data analysis, along with processing our data sets using various machine learning techniques. We gain a comprehensive understanding of data distribution and target patients’ characteristics through data visualization. Later, we use a wide range of different machine learning models, such as Decision Tree, Logistic Regression, Support Vector Classifier, Random Forest, Gradient Boosting, Adaptive Boosting, Bagging and K- Nearest Neighbor, etc. to get accurate predictions. Next, we combine the base models and apply various ensemble learning techniques to get even better performance. Extensive experiments have been conducted in our research, in which the ensemble of the Decision Tree + Logistic Regression model has shown its robustness and proven its effectiveness in thyroid detection by achieving 99.90% accuracy and maintaining a balance between precision and recall. Systematic analysis of different models provides a broad idea of the strengths and weaknesses of the models.