Artificial intelligence plays a crucial role in the healthcare system. Hence, machine learning algorithms could efficiently detect thyroid diseases early and help save lives. In this work, the proposed Three Stage Hybrid Classifier (3SHC) and Three Stage Hybrid Artificial Neural Network (3SHANN) significantly reduce the overfitting and underfitting issues due to the functionality of training models. However, the proposed 3SHC method achieves an accuracy of 99.29%, which outperforms the state-of-the-art models and shows that aged female people are more vulnerable to thyroid disease, significantly proving the existing literature. Also, the proposed method can be performed on a single CPU, efficiently solving the computational power limitation. Moreover, Local Interpretable Model-agnostic Explanations (LIME) are fitted with the classifier and features to explain the predicted outcomes and generate individual explanations. Thus, this method could be integrated into the Blockchain network i...

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used...