Predictive System for Pregnancy Risk and Maternal Health

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@article{1723774,
      author = {Aditi, Chaithanya K N, H Priyanka, Meghana N B, Arudra A; Soniya Komal V},
      title = {Predictive System for Pregnancy Risk and Maternal Health},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {897-903},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723774.pdf},
      abstract = {The Early detection of pregnancy and health of the fetus is important for health issues. Traditional approaches could need continuous monitoring and expertise in interpretation, making the process of early detection difficult. This paper presents a predictive system for fetal health and pregnancy risk assessment using machine learning. This system uses certain maternal and fetal health parameters in order to predict potential health conditions and risks involved.

The process used in this system consists of data collection, data pre-processing, feature analysis, machine learning model training, and prediction. This system works by using the health parameters given and predicting the fetal health condition and pregnancy risk using the patterns found in the dataset. Through the use of machine learning algorithms, this system attempts to offer faster and more accurate risk prediction. This is meant to help healthcare professionals detect cases requiring special attention.},
      keywords = {Fetal Health, Pregnancy Risk, Machine Learning, Predictive System, Fetal Monitoring, Risk Assessment.},
      month = {October},
  }