Automatic detection of fetal health status from cardiotocography data using machine learning algorithms

Md Tamjid Rayhan, ASM Shamsul Arefin*, Sabbir Ahmed Chowdhury

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

A method for the automatic determination of the fetus health status using Cardiotocography (CTG) and computer-based machine learning algorithms was developed. Five computation friendly machine learning algorithms were used to create multiclass classification models to predict the fetus health status from secondary CTG dataset containing normal, suspected and pathologic data available at University California Irvine Machine Learning Repository. Furthermore, a comparative analysis among the built models was executed. According to the comparative analysis, the best model to automatically detect fetal health was the extreme gradient boosting algorithm-based model with an accuracy of 96.7% and an F1-Score of 0.963 in the pathologic class. This finding thus has the potential to diagnose fetal heart conditions unsupervised, and more efficiently and effectively.
Original languageEnglish
Pages (from-to)155-167
Number of pages13
JournalJournal of Bangladesh Academy of Sciences
Volume45
Issue number2
DOIs
Publication statusPublished - 26 Jan 2022
Externally publishedYes

Keywords

  • cardiotocography
  • machine learning
  • fetal health
  • SDG
  • stillbirth

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