A wrapper-based approach for feature selection and classification of major depressive disorderbipolar disorders
We used the combination of support vector machine (SVM) and improved Ant Colony Optimization (IACO). We used an improved version of standard Ant Colony Optimization. We used coherence as a biomarker. We reduced the feature set from 48 to 22 using IACO. We modeled an SVM model using QEEG coherence values. We increased overall accuracy from 62.37% to 80.19% and AUC from 0.631 to 0.793 while decreasing the computational complexity and the number of features.
support vector machine improved ant colony optimization coherence artificial intelligence major depressive disorder bipolar disorder