Abstract: Novel systems and methods use extracted features from audio signals of patient breathing sounds taken during periods of full wakefulness in order to classify patients as either having, or not having, obstructive sleep apnea using a Random Forest machine learning algorithm. In some embodiments, the features are preprocessed then modeled. The models with a high correlation to the response and low overlap percentages are selected for use in the Random Forest classification process.