The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns
awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical
examination including (but not limited to) auscultation and pulse oximetry. While auscultation might
be subjective with some heart abnormalities not always audible it increases the ability to detect heart
defects. This work aims at developing an objective clinical decision support tool based on machine
learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus
(PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed
by feature extraction. The features are fed into a boosted decision tree classifier to estimate the
probability of PDA or CHDs. Several mechanisms to combine information from different auscultation
points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical
dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days
of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and
77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of
accuracy achieved by an experienced neonatologist when assessed on the same cohort