Open AccessUse of machine learning algorithms for prediction of fetal risk using cardiotocographic data
Author(s) -
Zahra Hoodbhoy,
Mohammad Noman,
Ayesha Shafique,
Ali Nasim,
Devyani Chowdhury,
Babar Hasan
Publication year - 2019
Publication title -
international journal of applied and basic medical research/international journal of applied and basic medical research
Language(s) - English
Resource type - Journals
eISSN - 2248-9606
pISSN - 2229-516X
DOI - 10.4103/ijabmr.ijabmr_370_18
Subject(s) - triage , machine learning , decision tree , suspect , random forest , medicine , artificial intelligence , gold standard (test) , referral , algorithm , computer science , pathological , medical emergency , psychology , criminology , family medicine
A major contributor to under-five mortality is the death of children in the 1 st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.