Trabecular bone fractures constitute a major health issue for the modern societies, with the currently established prediction methods of fracture risk, such as bone mineral density (BMD), resulting in errors up to 40%. Fracture-zone prediction based on bone's microstructure has been recently proposed as an alternative prediction method of fracture risk. In this paper, a classification system (CS) for the automatic fracture-zone prediction based on an Ensemble of Imbalanced Learning methods is proposed, following the observation that the percentage of the actual fractured bone area is significantly smaller than the intact bone in the case of a fracture event. The sample is divided into Volumes of Interest (VOIs) of specific size and 29 morphometrical parameters are calculated from each VOI, which serve as input features for the CS in order for it to separate the input patterns in to two classes: fractured and nonfractured. To this end, two well-established Imbalanced Learning methods, namely Random Undersampling and Synthetic Minority Oversampling, and two popular classification algorithms, namely Multilayer Perceptrons and Support Vector Machines, are tested and combined accordingly, to provide the best possible performance on a dataset that contains 45 specimens' pre- and postfailure scans. The best combination is then compared with three well-established Ensembles of Imbalanced Learning methods, namely RUSBoost, UnderBagging and SMOTEBagging. The experimental results clearly show that the proposed CS outperforms the competition, scoring in some occasions more than 90% in G-Mean and Area under Curve metrics. Finally, an investigation on the significance of the various trabecular bone's biomechanical parameters is made using the sequential forward floating selection technique, in order to identify possible biomarkers for fracture-zone prediction.