Secretary bird optimization meets deep learning: a hybrid machine learning approach for robust feature selection in high-dimensional medical data

Diagnosing thyroid disorders is challenging these days because of the large and complex data involved in medical records. The study uses a new machine learning setup where an SBO algorithm is used for feature selection along with CNN and LSTM networks. Guided by the secretary bird’s way of hunting, the Enhanced SBO method removes unnecessary and repeated attributes and leaves the most important ones. Because of these improvements, deep learning models now have the tools to handle the different and changing characteristics seen in patient data. Using open-source thyroid disease datasets, the paper confirms that this approach performs better than common ones in terms of accuracy, precision, recall, and the amount of resources needed. It is important to mention that the model can fit well into various groups of patients, suggesting that it could go into clinical use across a broad range of patients. Connecting nature-inspired algorithms with modern neural computing in this research helps create better and easier-to-understand diagnostic systems. It is suggested that future AI-based medical systems should use such hybrid intelligence in order to increase early detection and boost outcomes for patients suffering from endocrine disorders.