  Preterm labor is the leading cause of neonatal morbidity and mortality and
has attracted research efforts from many scientific areas. The
inter-relationship between uterine contraction and the underlying electrical
activities makes uterine electrohysterogram (EHG) a promising direction for
preterm detection and prediction. Due the scarcity of EHG signals, especially
those of preterm patients, synthetic algorithms are applied to create
artificial samples of preterm type in order to remove prediction bias towards
term, at the expense of a reduction of the feature effectiveness in
machine-learning based automatic preterm detecting. To address such problem, we
quantify the effect of synthetic samples (balance coefficient) on features'
effectiveness, and form a general performance metric by utilizing multiple
feature scores with relevant weights that describe their contributions to class
separation. Combined with the activation/inactivation functions that
characterizes the effect of the abundance of training samples in term and
preterm prediction precision, we obtain an optimal sample balance coefficient
that compromise the effect of synthetic samples in removing bias towards the
majority and the side-effect of reducing features' importance. Substantial
improvement in prediction precision has been achieved through a set of
numerical tests on public available TPEHG database, and it verifies the
effectiveness of the proposed method.
