Fetal health status prediction based on maternal clinical history using machine learning techniques.
Journal:
Computer methods and programs in biomedicine
Published Date:
Sep 1, 2018
Abstract
BACKGROUND AND OBJECTIVE: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60-70% of the anomalies can be diagnosed via ultrasonography, while the remaining 30-40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications.
Authors
Keywords
Algorithms
Area Under Curve
Bayes Theorem
Congenital Abnormalities
Decision Trees
Diagnosis, Computer-Assisted
Female
Fetus
Health Status
Humans
Internet
Logistic Models
Machine Learning
Mobile Applications
Perception
Pregnancy
Regression Analysis
Reproducibility of Results
ROC Curve
Support Vector Machine
Telemedicine
Ultrasonography, Prenatal