Firefly Algorithm-Based Feature Selection for Accurate Prediction of Major Depressive Disorder from EEG Signals

Around the world, billions of people suffer from Major Depressive Disorder, a complex mental health issue. Most psychological assessments are subjective, and symptoms can fluctuate widely, making the diagnosis of Major Depressive Disorder challenging. Improved diagnostic accuracy can be achieved through innovative approaches like machine learning and optimization. In this study, we present the Firefly Algorithm, a nature-inspired metaheuristic, to enhance feature selection and classification for MDD diagnosis. Firefly-adapted artificial intelligence identifies the most relevant elements that lead to a correct diagnosis, adapting to the complexity of MDD datasets. Using real-world MDD datasets, we evaluate the FA's effectiveness compared to conventional machine learning methods. The results demonstrate that the Firefly Algorithm is a promising approach for improved MDD diagnosis, as it reduces computational complexity and boosts diagnostic accuracy. Specifically, the FA-optimized SVM model (96.24%) achieved the highest level of diagnostic accuracy, followed by KNN, ANN, QDA, and Naïve Bayes. These findings highlight how the FA can select the most suitable characteristics to yield more accurate and efficient models, potentially enhancing MDD diagnosis