An Intelligent Technique for Thyroid Disease Detection Using Machine Learning

Abstract

An underactive thyroid gland is the hallmark of the common endocrine condition hypothyroidism, which can cause a variety of health problems. A timely and precise diagnosis is essential for the proper management and treatment of this illness. In this paper, we investigate how machine learning approaches—more especially, ensemble techniques like Bagging and Boosting—can be used to forecast hypothyroidism. We have taken two popular datasets from Kaggle and Figshare website. We use a wide range of data, such as laboratory and clinical characteristics, to train and assess various machine learning models. The Bagging method lowers variance and improves overall model stability by combining predictions from several base learners. By giving misclassified cases a larger weight, the technique known as "boosting" aims to repeatedly improve the model's accuracy. The most accurate classifier was the traditional technique, which achieved an impressive accuracy rate of 93.17% by Random Forest (RF). Other classifiers that were used included Logistic Gradient Boosting (GB), Regression (LR), Adaboost Classifier (ABC), K-Nearest Classifier (KN), Support Vector Machine (SVM), Decision Tree (DT), Ridge Classifier (RC), Quadratic Discriminant Analysis (QDA), Passive Aggressive (PA), Gaussian Naïve Bayes (GNB). In addition, 92.15% accuracy was obtained by the Boosting Gradient Boosting (GB), while Boosting Random Forest (RF) 91.86% accuracy was attained. Hyperparameter tweaking was used to maximize each classifier's performance. After conducting an experimental examination and reviewing prior research, it was determined that the Random Forest (RF) classifier performed very well, correctly diagnosing hypothyroid illness with an astounding accuracy rate of 93.17%.