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<p>The cardiotocogram (CTG) is a technique used to evaluate the health of the baby during pregnancy. Uterine contractions (UC) and Fetal Heart Rate (FHR) are the two main outcomes of CTG. Twenty-one parameters, including baseline values, accelerations, foetal movements, light decelerations, etc., are included in the assessment of FHR and UC on CTG. Obstetricians can use these parameters to help them assess if the fetus’s health is normal, suspicious, or abnormal. A variety of machine learning methods can be used to assess the health of the fetus. This study looks at the K-Nearest Neighbor (KNN), Decision Tree, Random Forest (RF), Gradient Boosting and proposed model PCBoost (Principal Component Analysis-Gradient Boosting) for predicting fetal hypoxia. This study also compares the performance metrics (precision, recall, F1 score, accuracy and kappa score) of all the above algorithms. The proposed model uses Principal Component Analysis (PCA) implementation for dataset size reduction to assess the performance. The study produces positive findings, as the proposed PCBoost model achieves a maximum accuracy of 99% under Scenario S4, where PCA retains 97% variance using 16 components, outperforming other machine learning techniques. While the current study relies on the UCI CTG dataset for evaluation, efforts were made to prevent overfitting through PCA and cross-validation techniques. Future work will include validation on additional clinical datasets to enhance model robustness and clinical applicability. This illustrates how well the proposed models enhance the precision of the CTG-based classification of fetal health. The results support the expediting of fetal health evaluations by incorporating machine learning models into standard clinical procedures. The study emphasizes the need of early issue detection while demonstrating how machine learning (ML) may be utilized to maximize medical resource allocation and time efficiency.</p>

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An Approach of ML Model to Detect Fetal Hypoxia for Fetal Health Status from Cardiotocogram Data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Kauleshwar Prasad,
  • Pawan Kumar Patnaik,
  • Anupam Agrawal,
  • Narendra Khatri

摘要

The cardiotocogram (CTG) is a technique used to evaluate the health of the baby during pregnancy. Uterine contractions (UC) and Fetal Heart Rate (FHR) are the two main outcomes of CTG. Twenty-one parameters, including baseline values, accelerations, foetal movements, light decelerations, etc., are included in the assessment of FHR and UC on CTG. Obstetricians can use these parameters to help them assess if the fetus’s health is normal, suspicious, or abnormal. A variety of machine learning methods can be used to assess the health of the fetus. This study looks at the K-Nearest Neighbor (KNN), Decision Tree, Random Forest (RF), Gradient Boosting and proposed model PCBoost (Principal Component Analysis-Gradient Boosting) for predicting fetal hypoxia. This study also compares the performance metrics (precision, recall, F1 score, accuracy and kappa score) of all the above algorithms. The proposed model uses Principal Component Analysis (PCA) implementation for dataset size reduction to assess the performance. The study produces positive findings, as the proposed PCBoost model achieves a maximum accuracy of 99% under Scenario S4, where PCA retains 97% variance using 16 components, outperforming other machine learning techniques. While the current study relies on the UCI CTG dataset for evaluation, efforts were made to prevent overfitting through PCA and cross-validation techniques. Future work will include validation on additional clinical datasets to enhance model robustness and clinical applicability. This illustrates how well the proposed models enhance the precision of the CTG-based classification of fetal health. The results support the expediting of fetal health evaluations by incorporating machine learning models into standard clinical procedures. The study emphasizes the need of early issue detection while demonstrating how machine learning (ML) may be utilized to maximize medical resource allocation and time efficiency.