Osteoporosis is a major public health problem;​ it is characterized by a loss in bone connectivity, which leads to a higher risk of fracture. The objective of this article is to develop a new connectivity parameter for bone microarchitecture characterization and osteoporosis assessment. The purpose is to discriminate 164 subjects composed of 82 healthy patients (HL) and 82 osteoporotic cases (OP). The new connectivity parameter involves several new topological features. The proposed method was compared to a traditional connectivity index, and the results reveal the superiority and the outperformance of the new parameter to discriminate the two groups of subjects with an accuracy (Acc) of 71.95 % and area under curve (AUC) of 80.03 %. Moreover, clinical parameters from patients were involved in this study, and five configurations were constructed, tested, and validated on the data using the k-fold cross-validation (CV) model with several values of k. Furthermore, support vector machine (SVM) was used and various kernels (i.e., linear, quadratic, cubic, and RBF functions) were tested in this study. The objective is to look for the configuration providing the best performance in terms of separation between the two populations. Furthermore, several classifiers (logistic regression, k-nearest neighbors, boosted trees, and naïve Bayes) were tested and a combination of these classifiers was carried out using the stacking ensemble technique to improve the accuracy of the final prediction. Moreover, several studies of state-of-the-art were compared to the proposed method. The results obtained reveal that the 10-fold CV approach combining the new trabecular connectivity index and RBF function of SVM achieved the highest accuracy with Acc = 88.41 %, and AUC = 95.24 %. In addition, the proposed ensemble Meta classifier improved the accuracy of SVM and achieved a high rate with Acc = 95.12 % and AUC = 98.40 % outperforming the existing methods in the literature.