An Intelligent System for Thyroid Dysfunction Prediction

Abstract

The prediction of thyroid diseases has become increasingly important in recent years. The feature engineering part is generally less studied in existing approaches than model optimization. To circumvent these restrictions, feature engineering for machine learning (ML) and deep learning models are examined in our research. This paper proposes a system to predict thyroid dysfunction. A model comprises three stages: preprocessing of the data, selection of features, and classification. A standard scalar normalizes the data after filling missing values with mean values. A Deep Neural Network (DNN) is utilized for classification, and the Improved Salp Swarm Algorithm (ISSA) is used for feature selection. Finally, accuracy is utilized to estimate the system proposed. The UCI thyroid illness datasets are used in our investigation. In experiments with the DNN model, deep learning classifiers based on selected features produce the highest accuracy of 99.68%. The model proposed has superior performance compared to state-of-the-art methods.