Early prediction of hypothyroidism and multiclass classification using predictive machine learning and deep learning

Abstract

Thyroid disease is considered one of the most common health disorders, which may lead to various health problems. Recent studies reveal that approximately 42 million people in India face thyroid dysfunction or disorder problems. The thyroid hormone is responsible for thyroid disorder which may lead to hypothyroidism or hyperthyroidism problems. TSH (Thyroid Stimulating Hormone), T3 (Triiodothyronine, T3-RIA), FT4 (FT4, Free Thyroxine), T4 (Thyroxine), FTI (Free Thyroxine Index, FTI, T7) are the significant components of thyroid test which is performed to diagnose the behavior of thyroid hormone. However, manual analysis of these parameters on large databases to diagnose and predict hypothyroidism or hyperthyroidism is tedious. In this article, various machine learning-based techniques have been applied to build predictive models, which includes decision tree, random forest, naive Bayes and multiclass classifier and a deep learning (DL) based model Artificial Neural Network (ANN), which is best known for dealing with text data has been applied to predict the class of hypothyroidism. The performance evaluation indicates that the decision tree and random forest provide better results with the highest accuracy of 99.5758% and 99.3107% and very few error rates of 0.0424 and 0.0689, respectively. Furthermore, a comparison among the presented classifiers has been made, and also the proposed model has been compared with previous works, and it has been found that it shows better accuracy as compared to other related works. The DL-based ANN model also offers a competitive accuracy which is 93.8226%. Furthermore, this study can be useful for researchers to identify a suitable model for hypothyroidism detection and classification.

Keywords

• Machine learning (ML)
• Deep learning (DL)
• ANN
• Decision tree
• Random forest
• Multi-class classification