A Novel Hybrid Model For Automated Analysis Of Cardiotocograms Using Machine Learning Algorithms
DOI:
https://doi.org/10.18201/ijisae.2021473716Keywords:
Biomedical diagnostics, Machine Learning algorithms, Fetal heart rate measurement.Abstract
In this study, a new hybrid model was presented for the prediction of fetal state from fetal heart rate (FHR) and the uterine contraction (UC) signals obtained from cardiotocogram (CTG) recordings. CTG monitoring of FHR and uterine contractions during pregnancy and delivery provides information on the physiological status of the fetus to identify hypoxia. Accurate information from these records can be used to estimate the pathological condition of the fetus. Thus, it allows early intervention by reporting any irreversible negative condition in the fetus. In this study, due to the importance of this subject, a new hybrid model was developed which can perform high rate accurate diagnosis using Machine Learning (ML) algorithms. In the hybrid model, 4 different ML algorithms (k Nearest Neighbors (k-NN), Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM)) were used. While the diagnosis without the hybrid model was low, the improved hybrid model increased the accuracy by 34%. As a result of this hybrid model, 100% success was achieved for classification, test success, Accuracy, Sensitivity and Specificity with NB and DT ML algorithms.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.