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Fetal health classification from cardiotocographic data using machine learning.

Authors :
Mehbodniya, Abolfazl
Lazar, Arokia Jesu Prabhu
Webber, Julian
Sharma, Dilip Kumar
Jayagopalan, Santhosh
K, Kousalya
Singh, Pallavi
Rajan, Regin
Pandya, Sharnil
Sengan, Sudhakar
Source :
Expert Systems. Jul2022, Vol. 39 Issue 6, p1-13. 13p.
Publication Year :
2022

Abstract

Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02664720
Volume :
39
Issue :
6
Database :
Academic Search Index
Journal :
Expert Systems
Publication Type :
Academic Journal
Accession number :
157616525
Full Text :
https://doi.org/10.1111/exsy.12899