A novel fuzzy decision tree-based SVM (FDT-SVM) classifier is proposed in this paper, to distinguish between asymptotic (AS) and osteoarthritis (OA) knee gait patterns and to investigate OA severity using 3-D ground reaction force (GRF) measurements. FDT-SVM incorporates effective techniques for feature selection (FS) and class grouping (CG) at each non-leaf nodes of the tree structure, which reduce the overall complexity of DT building and alleviate the overfitting effect. The embedded FS and CG are based on the notion of fuzzy partition vector (FPV) that comprises the fuzzy membership degrees of every pattern in their target classes, serving as a local evaluation metric with respect to patterns. FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that features are iteratively introduced, providing the maximum additional contribution in regard to the information content given by the previously selected features. A novel Wavelet Packet (WP) decomposition based on the FuzCoC principles is also introduced, to distinguish informative and complementary features from GRF data. The quality of our method is validated in terms of statistical metrics drawn by confusion matrices, such as sensitivity, specificity and total classification accuracy. In addition, we investigate the impact of each GRF component. Finally, comparative results with existing techniques are given, demonstrating the efficacy of the suggested approach.