  Perinatal Asphyxia is one of the top three causes of infant mortality in
developing countries, resulting to the death of about 1.2 million newborns
every year. At its early stages, the presence of asphyxia cannot be
conclusively determined visually or via physical examination, but by medical
diagnosis. In resource-poor settings, where skilled attendance at birth is a
luxury, most cases only get detected when the damaging consequences begin to
manifest or worse still, after death of the affected infant. In this project,
we explored the approach of machine learning in developing a low-cost
diagnostic solution. We designed a support vector machine-based pattern
recognition system that models patterns in the cries of known asphyxiating
infants (and normal infants) and then uses the developed model for
classification of `new' infants as having asphyxia or not. Our prototype has
been tested in a laboratory setting to give prediction accuracy of up to
88.85%. If higher accuracies can be obtained, this research may be a key
contributor to the 4th Millennium Development Goal (MDG) of reducing mortality
in under-five children.
