ENHANCING RECURRENT NEURAL NETWORK PERFORMANCE FOR LATENT AUTOIMMUNE DIABETES DETECTION (LADA) USING EXOCOETIDAE OPTIMIZATION

Latent Autoimmune Diabetes in Adults (LADA) is frequently misclassified as type 2 diabetes, a significant issue highlighted in current diagnostic research. This misclassification, due to overlapping clinical features with other diabetes types, leads to suboptimal patient management. Existing models lack the precision to discern the subtle, time-dependent variations characteristic of LADA, underscoring a critical gap in diabetes diagnostics. This study introduces a groundbreaking approach, the Exocoetidae Optimization-Inspired Recurrent Neural Network (EO-RNN), which innovatively incorporates the adaptive flight patterns of Exocoetidae (flying fish) into optimizing RNNs. This bio-inspired algorithm enhances parameter tuning and model adaptability, significantly improving the detection accuracy of complex disease patterns. Employing the EO-RNN on the Gestational Diabetes Mellitus (GDM) dataset resulted in a classification accuracy of 95.36% and an F-measure of 95.47%, surpassing traditional models by over 30%. These results not only bridge the identified literature gap by providing a more effective diagnostic tool but also contribute substantial new knowledge about the integration of nature-inspired algorithms in medical diagnostics. The success of EO-RNN in enhancing diagnostic accuracy demonstrates its potential as a transformative tool in healthcare, promising for broader applications in detecting complex diseases where similar misclassification issues exist.