SQUIRREL SEARCH GRADIENT OPTIMIZED DEEP BELIEF NETWORK CLASSIFIER FOR THYROID DISEASE PREDICTION

Thyroid disease is a range of disorders that affect the thyroid gland, a butterfly-shaped organ located in the neck responsible for producing hormones that regulate metabolism, energy levels, and overall bodily functions. Early detection and management of thyroid disease are crucial, as untreated conditions leads to severe complications, including cardiovascular issues, infertility, and metabolic disorders. Advanced diagnostic methods, including machine learning and deep learning techniques, are increasingly used to improve the accuracy and timeliness of thyroid disease detection, facilitating better treatment outcomes. But, severity of thyroid disease prediction accuracy with minimal time is major challenging issues. In order to improve the accuracy of thyroid disease prediction, a novel Squirrel Search Gradient Optimized Deep Belief Neural Classifier (SSGODBNC) model is developed with minimal time consumption. The proposed Deep Belief Network (DBN) is a fully connected artificial feed-forward deep learning method comprising two visible layers such as the input and output layer and multiple hidden layers for processing the given input. In the layer-by-layer process, the first hidden layer receives weighted input and performs data preprocessing. Then extracting significant features and eliminates the insignificant features from the dataset using the Sparse Autoencoder model. These selected significant features are utilized to classify the severity level of thyroid disease using Sokal–Michener’s simple matching method. During fine-tuning, error back-propagation algorithms adjust the hyperparameters using Squirrel Search Gradient Optimization to increase the accuracy of thyroid disease classification. This optimized fine-tuning process significantly enhances the performance of the deep belief network and improves overall learning efficiency in classification tasks. Finally, the accurate thyroid disease severity prediction results with minimal error are obtained at the output layer. Experimental assessment is conducted with different evaluation metrics such as Accuracy, Precision, Recall, F1-score, specificity and Thyroid disease prediction time. The observed result shows the effectiveness of the proposed SSGODBNC model with higher accuracy in thyroid disease prediction with minimum time than the existing methods.