One　of　the　remaining　challenges　for　the　scientific-technical　community　is　predicting　preterm　births,　for　which　electrohysterography　(EHG)　has　emerged　as　a　highly　sensitive　prediction　technique.　Sample　and　fuzzy　entropy　have　been　used　to　characterize　EHG　signals,　although　they　require　optimizing　many　internal　parameters.　Both　bubble　entropy,　which　only　requires　one　internal　parameter,　and　dispersion　entropy,　which　can　detect　any　changes　in　frequency　and　amplitude,　have　been　proposed　to　characterize　biomedical　signals.　In　this　work,　we　attempted　to　determine　the　clinical　value　of　these　entropy　measures　for　predicting　preterm　birth　by　analyzing　their　discriminatory　capacity　as　an　individual　feature　and　their　complementarity　to　other　EHG　characteristics　by　developing　six　prediction　models　using　obstetrical　data,　linear　and　non-linear　EHG　features,　and　linear　discriminant　analysis　using　a　genetic　algorithm　to　select　the　features.　Both　dispersion　and　bubble　entropy　better　discriminated　between　the　preterm　and　term　groups　than　sample,　spectral,　and　fuzzy　entropy.　Entropy　metrics　provided　complementary　information　to　linear　features,　and　indeed,　the　improvement　in　model　performance　by　including　other　non-linear　features　was　negligible.　The　best　model　performance　obtained　an　F1-score　of　90.1　+/-　2%　for　testing　the　dataset.　This　model　can　easily　be　adapted　to　real-time　applications,　thereby　contributing　to　the　transferability　of　the　EHG　technique　to　clinical　practice.