HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning
Journal:
arXiv
Published Date:
Dec 2, 2024
Abstract
Birth Apshyxia (BA) is a severe condition characterized by insufficient
supply of oxygen to a newborn during the delivery. BA is one of the primary
causes of neonatal death in the world. Although there has been a decline in
neonatal deaths over the past two decades, the developing world, particularly
sub-Saharan Africa, continues to experience the highest under-five (<5)
mortality rates. While evidence-based methods are commonly used to detect BA in
African healthcare settings, they can be subject to physician errors or delays
in diagnosis, preventing timely interventions. Centralized Machine Learning
(ML) methods demonstrated good performance in early detection of BA but require
sensitive health data to leave their premises before training, which does not
guarantee privacy and security. Healthcare institutions are therefore reluctant
to adopt such solutions in Africa. To address this challenge, we suggest a
federated learning (FL)-based software architecture, a distributed learning
method that prioritizes privacy and security by design. We have developed a
user-friendly and cost-effective mobile application embedding the FL pipeline
for early detection of BA. Our Federated SVM model outperformed centralized SVM
pipelines and Neural Networks (NN)-based methods in the existing literature
