Article

Unveiling the unborn: Advancing fetal health classification through machine learning

2023-12-26

Abstract excerpt

Fetal health classification is a critical task in obstetrics, which enables early identification and management of potential health problems. However, it remains a challenging task due to data complexity and limited labeled samples. This research paper presents a novel machine-learning approach for fetal health classification, leveraging a LightGBM classifier trained on a comprehensive dataset. The proposed model...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 25af294f-4771-5b97-84b7-90ee3b502335
DOI: 10.36922/aih.2121

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Unveiling the unborn: Advancing fetal health classification through machine learningDOI 10.36922/aih.2121Read

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Semantic proximity Read

Machine learning to predict outcomes of fetal cardiac disease: a pilot studySemantic proximity Read

Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birthSemantic proximity Read

Deep Learning-Based Inference of Uterine Contractions from Maternal ECGSemantic proximity Read

Optimizing Screening for Intrauterine Fetal Growth Restriction in Low-Resource Settings Using 2D Ultrasound: A Deep Learning ApproachSemantic proximity Read

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second TrimestersSemantic proximity Read

Reducing stillbirth in high burden settings using biomarkers and ultrasound technologies: protocol for the multi-centre prospective iTECH cohort studySemantic proximity Read

Cardiotocography-dependent experimental comparison between artificial intelligence and human judgement of fetal asphyxia during deliverySemantic proximity Read

Artificial Intelligence in Obstetrics: Current Trends and Future DirectionsSemantic proximity Read

Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signalsSemantic proximity Read

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenariosSemantic proximity Read

Preterm Birth Risk Stratification through Longitudinal Heart Rate and HRV Monitoring in Daily LifeSemantic proximity Read