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 are exploring the approach of machine learning in developing a low-cost diagnostic solution that can determine newborns with asphyxia by analysing their cry. This solution, which we are working on in collaboration with researchers from the Department of Public Health at the Federal University of Technology, Owerri, will provide as much as 98% saving over the current clinical method of diagnosis and has undergone laboratory testing to give promising prediction accuracies of up to 89%.