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Health complications of a fetus in its developing stage are a major issue. Ensuring the well-being of fetus during gestation is important for the health of the mother as well as the child. Preserving the fetus’s heart health has a significant influence on assessing its overall health. The prediction of health outcomes using data collected from medical tests has been greatly advanced in recent years by machine learning (ML) and deep learning (DL). Our paper demonstrates the use of certain ML as well as DL algorithms to predict fetal abnormalities from cardiotocography (CTG) results and find out which model will provide better result for its input data. The comparison of the results of various models is done based on its accuracy, F1-score, precision, and recall.

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Fetal Health Prediction from Cardiotocographic Data

  • 会议论文

  • 发表日期：

    2025年

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  • Raji Ramachandran,
  • K. Abhijith,
  • J. J. Karthik

摘要

Health complications of a fetus in its developing stage are a major issue. Ensuring the well-being of fetus during gestation is important for the health of the mother as well as the child. Preserving the fetus’s heart health has a significant influence on assessing its overall health. The prediction of health outcomes using data collected from medical tests has been greatly advanced in recent years by machine learning (ML) and deep learning (DL). Our paper demonstrates the use of certain ML as well as DL algorithms to predict fetal abnormalities from cardiotocography (CTG) results and find out which model will provide better result for its input data. The comparison of the results of various models is done based on its accuracy, F1-score, precision, and recall.