In recent years, osteoporosis prediction has been paid more attention among healthcare experts and the public. It is a silent disease that causes many fractures and complications that impact the quality of human life; therefore, predicting osteoporosis is important to reduce the risk of fractures; however, many irrelevant descriptors can influence the prediction of osteoporosis, thus, computational methods are needed. In this article, we present a new method to predict osteoporosis, which starts by pre-processing the data to avoid an imbalanced dataset. Then, the sine–cosine algorithm, based on the information gain fuzzy-rough set, is applied to select the most discriminative descriptors. Finally, classifiers are used to predict the deficiency of osteoporosis samples based on the selected descriptors. To evaluate the efficacy of the proposed approach, two experiments were performed using benchmark datasets and real osteoporosis data. The results of the experiments show that the proposed approach achieved competitive results compared to the other methods in selecting the most appropriate descriptors for predicting osteoporosis. The selected descriptors show a high correlation with osteoporosis.