Optimizing Fetal health Classification with PCA and SMOTE Techniques

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 %.