Optimizing LSTM-Based Model with Ant-Lion Algorithm for Improving Thyroid Prognosis

the healthcare sector, early and accurate disease detection is essential for providing appropriate care on time. This is especially crucial in thyroid problems, which can be difficult to diagnose because of their many symptoms. This study aims to propose a new thyroid disease prediction model by utilizing the Ant Lion Optimization (ALO) approach to enhance the hyperparameters of the Long Short-Term Memory (LSTM) deep learning algorithm. To achieve this, after the preprocessing step, we utilize the entropy technique for feature selection, which selects the most important features as an optimal subset of features. The ALO is then employed to optimize the LSTM, identifying the optimal hyperparameters that can influence the model and enhance its efficiency. To assess the suggested methodology, we chose the widely used thyroid disease data. This dataset contains 9,172 samples and 31 features. A set of criteria was used to evaluate the model's performance, including accuracy, precision, recall, and F1 score. The experimental results showed that: 1) the entropy technique in the feature selection step can reduce the total number of features from 31 to 10; 2) the recommended strategy, which selected the optimal hyperparameter for the LSTM using the Alo algorithm, improved the classifier overall by 7.2% and produced the highest accuracy of 98.6%.