Machine learning framework with feature selection approaches for thyroid disease classification and associated risk factors identification

Abstract

Thyroid disease (TD) develops when the thyroid does not generate an adequate quantity of thyroid hormones as well as when a lump or nodule emerges due to aberrant growth of the thyroid gland. As a result, early detection was pertinent in preventing or minimizing the impact of this disease. In this study, different machine learning (ML) algorithms with a combination of scaling method, oversampling technique, and various feature selection approaches have been applied to make an efficient framework to classify TD. In addition, significant risk factors of TD were also identified in this proposed system. The dataset was collected from the University of California Irvine (UCI) repository for this research. After that, in the preprocessing stage, Synthetic Minority Oversampling Technique (SMOTE) was used to resolve the imbalance class problem and robust scaling technique was used to scale the dataset. The Boruta, Recursive Feature Elimination (RFE), and Least Absolute Shrinkage and Selection Operator (LASSO) approaches were used to select appropriate features. To train the model, we employed six different ML classifiers: Support Vector Machine (SVM), AdaBoost (AB), Decision Tree (DT), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Random Forest (RF). The models were examined using a 5-fold CV. Different performance metrics were observed to compare the effectiveness of the algorithms. The system achieved the most accurate results using the RF classifier, with 99% accuracy. This proposed system will be beneficial for physicians and patients to classify TD as well as to learn about the associated risk factors of TD.