Objective　The　objective　of　this　study　was　to　investigate　the　effectiveness　of　a　machine　learning　algorithm　in　diagnosing　OSA　in　children　based　on　clinical　features　that　can　be　obtained　in　nonnocturnal　and　nonmedical　environments.Patients　and　methods　This　study　was　conducted　at　Beijing　Children＇s　Hospital　from　April　2018　to　October　2019.　The　participants　in　this　study　were　2464　children　aged　3-18　suspected　of　having　OSA　who　underwent　clinical　data　collection　and　polysomnography(PSG).　Participants＇　data　were　randomly　divided　into　a　training　set　and　a　testing　set　at　a　ratio　of　8:2.　The　elastic　net　algorithm　was　used　for　feature　selection　to　simplify　the　model.　Stratified　10-fold　cross-validation　was　repeated　five　times　to　ensure　the　robustness　of　the　results.Results　Feature　selection　using　Elastic　Net　resulted　in　47　features　for　AHI　＞=　5　and　31　features　for　AHI　＞=　10　being　retained.　The　machine　learning　model　using　these　selected　features　achieved　an　average　AUC　of　0.73　for　AHI　＞=　5　and　0.78　for　AHI　＞=　10　when　tested　externally,　outperforming　models　based　on　PSG　questionnaire　features.　Linear　Discriminant　Analysis　using　the　selected　features　identified　OSA　with　a　sensitivity　of　44%　and　specificity　of　90%,　providing　a　feasible　clinical　alternative　to　PSG　for　stratifying　OSA　severity.Conclusions　This　study　shows　that　a　machine　learning　model　based　on　children＇s　clinical　features　effectively　identifies　OSA　in　children.　Establishing　a　machine　learning　screening　model　based　on　the　clinical　features　of　the　target　population　may　be　a　feasible　clinical　alternative　to　nocturnal　OSA　sleep　diagnosis.