Due　to　its　high　sensitivity,　electrohysterography　(EHG)　has　emerged　as　an　alternative　technique　for　predicting　preterm　labor.　The　main　obstacle　in　designing　preterm　labor　prediction　models　is　the　inherent　preterm/term　imbalance　ratio,　which　can　give　rise　to　relatively　low　performance.　Numerous　studies　obtained　promising　preterm　labor　prediction　results　using　the　synthetic　minority　oversampling　technique.　However,　these　studies　generally　overestimate　mathematical　models＇　real　generalization　capacity　by　generating　synthetic　data　before　splitting　the　dataset,　leaking　information　between　the　training　and　testing　partitions　and　thus　reducing　the　complexity　of　the　classification　task.　In　this　work,　we　analyzed　the　effect　of　combining　feature　selection　and　resampling　methods　to　overcome　the　class　imbalance　problem　for　predicting　preterm　labor　by　EHG.　We　assessed　undersampling,　oversampling,　and　hybrid　methods　applied　to　the　training　and　validation　dataset　during　feature　selection　by　genetic　algorithm,　and　analyzed　the　resampling　effect　on　training　data　after　obtaining　the　optimized　feature　subset.　The　best　strategy　consisted　of　undersampling　the　majority　class　of　the　validation　dataset　to　1:1　during　feature　selection,　without　subsequent　resampling　of　the　training　data,　achieving　an　AUC　of　94.5　+/-　4.6%,　average　precision　of　84.5　+/-　11.7%,　maximum　F1-score　of　79.6　+/-　13.8%,　and　recall　of　89.8　+/-　12.1%.　Our　results　outperformed　the　techniques　currently　used　in　clinical　practice,　suggesting　the　EHG　could　be　used　to　predict　preterm　labor　in　clinics.