Research Article
Predicting Fetal Condition from Cardiotocography Results Using the Random Forest Method
@INPROCEEDINGS{10.4108/eai.12-10-2019.2296540, author={Syifa Fauziyah Nurul Islam and Intan Nurma Yulita}, title={Predicting Fetal Condition from Cardiotocography Results Using the Random Forest Method}, proceedings={Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia}, publisher={EAI}, proceedings_a={MSCEIS}, year={2020}, month={7}, keywords={cardiotocography radom forest naive bayes decision tree}, doi={10.4108/eai.12-10-2019.2296540} }- Syifa Fauziyah Nurul Islam
Intan Nurma Yulita
Year: 2020
Predicting Fetal Condition from Cardiotocography Results Using the Random Forest Method
MSCEIS
EAI
DOI: 10.4108/eai.12-10-2019.2296540
Abstract
Cardiotocography is an important process in pregnancy as fetal monitoring. It monitors the baby's heart rate in a healthy condition or not. Apart from that, this can also measure whether the movements carried out by the baby in the womb are normal or not. This study extracted the recording data by cardiotocographs. The attributes of fetal data that have been recorded amount to 22. They were used as the indicators in determining the conditions of the fetus whether under normal circumstances, suspect or pathologic. The prediction of the fetus condition was based on the Random Forest method. Also, the method was compared with the Naïve Bayes and Decision Tree methods. The accuracy of the Random Forest method reached 95.11%. It was higher compared to using other methods.