Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory diseases caused by demyelination and axonal damage in the central nervous system. Structural retinal imaging via optical coherence tomography (OCT) shows promise as a biomarker for monitoring of MS. There are successful reports regarding application of Artificial Intelligence (AI) in analysis of cross-sectional OCTs in ophthalmologic diseases. However, the alteration of sub-retinal thicknesses in MS are noticeably subtle compared to other ophthalmologic diseases. Therefore, raw cross-sectional OCTs are replaced with multilayer segmented OCTs for discrimination of MS and heathy controls (HCs). To conform to the principles of trustworthy AI, interpretability is provided by visualizing regional layer contribution to classification performance with proposed occlusion sensitivity approach. The robustness of the classification is also guaranteed by showing the effectiveness of the algorithm while being tested on the new independent (but similar) dataset. The most discriminative features from different topologies of the multilayer segmented OCTs are selected by dimension reduction methods. Support vector machine (SVM), random forest (RF), and artificial neural network (ANN) are used for classification. Patient-wise cross-validation (CV) is utilized to evaluate the performance of the algorithm, where the training and test folds contain records from different subjects. The most discriminative topology is determined to be squares with side of 40 pixels and the most influential sub-retinal layers are ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL). Linear SVM resulted in 88% Accuracy, 78% precision and 63% recall in discrimination of MS and HCs using macular multilayer segmented OCTs.