Maternal Risk App

This dataset comprises detailed records of 76,645 pregnancies collected from multiple healthcare centers, designed to support the development of predictive models for maternal risk stratification. Each row corresponds to one pregnant individual and includes: Demographic Features Age: Maternal age in years. Body Mass Index (BMI):Calculated at first prenatal visit. Obstetric History Gestational Age at Enrollment: Number of weeks since last menstrual period. Parity: Number of prior births beyond 20 weeks. Previous Cesarean Sections and Vaginal Births: Count of each type. History of Preterm Birth or Miscarriage Pre-existing Conditions Chronic hypertension Pre-gestational diabetes Autoimmune disorders Prior preeclampsia or eclampsia Clinical Measurements (First and Third Trimester) Systolic and diastolic blood pressure Hemoglobin (g/dL) Random blood glucose (mg/dL) Urine protein level (dipstick reading) Lifestyle and Family History Smoking status (current/former/never) Alcohol use during pregnancy Family history of gestational diabetes or hypertensive disorders Outcome Label Risk Level: Categorized as “Low,” “Moderate,” or “High” based on composite maternal and fetal outcome criteria (e.g., preeclampsia, preterm delivery, severe hemorrhage) . Potential Applications: Machine Learning: Train classification algorithms to predict risk categories early in pregnancy. Feature Analysis: Identify key predictors of adverse maternal outcomes and quantify their importance. Clinical Decision Support Integrate risk scores into health records for closer monitoring of high-risk cases. Epidemiological Studies Explore links between demographics, lifestyle, and maternal complications. By offering a rich blend of historical, clinical, and demographic variables, this dataset serves as a robust foundation for both data science research and real‑world healthcare applications aimed at improving maternal and neonatal outcomes.