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Snoring is an important characteristic of obstructive sleep apnea-hypopnea syndrome (OSAHS). Snoring sound can be used to develop a non-invasive approach for automatically screening OSAHS. In this paper, a snoring detection algorithm based on acoustic features and the XGBoost was proposed. The Fisher Ratio (FR) method was used for feature selection and the 23-dimensional feature set was extracted from the original 58-dimensional feature to reduce computational complexity. A variety of training and test data split methods were used to evaluate the performance of the proposed model. In addition, the proposed method was compared with the deep learning method on the independent subjects training set and test set. The accuracy of the reduced feature set by the proposed model was 87.22% and the deep learning model 87.26% on independent subjects training set and test set, the prediction time of the deep learning model on the test set was around 238 ms, the prediction time of XGBoost was around 58 ms, and the computing efficiency of XGBoost is higher than that of the deep learning model. The results show that the proposed method achieves a good balance between computational cost and performance. These results provide an important method for fast and real-time classification and evaluation of snoring for OSAHS. [ABSTRACT FROM AUTHOR]