Optimizing Fetal Health Classification with PCA and SMOTE Techniques
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Abstract
Cardiotocography (CTG) is used in pregnancy to monitor fetal heart rate and contractility, especially in the third
trimester, to ensure fetal well-being and to detect early signs of distress CTG a inconsistency may indicate the need for further
research and possible interventions. The objective is to increase the accuracy and reliability of cervical health classification by
integrating machine learning algorithms with traditional CTG data. This approach seeks to improve the early detection of fetal
distress to identify timely medical interventions. The system combines CTG data collection with machine learning algorithms to
identify fetal health risks. It uses transducers to monitor fetal heart rate and contractions. Machine learning models are used to
analyze CTG data, such as random forest, logistic regression, decision tree and KNN, the results showed that the random forest
model outperformed the others, achieving an accuracy of 97.58 %.
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