Maternal Health Risk Assessment Dataset
Description
This dataset comprises detailed clinical, physiological, and historical health information collected from maternal patients to evaluate potential health risks during pregnancy. It serves as a resource for developing predictive models aimed at identifying and managing high-risk pregnancies, providing insights into maternal health factors, and supporting personalized patient care. The dataset is well-suited for research in obstetrics, predictive health modeling, and maternal healthcare management. Key Features: Age: Age of the patient, which can be a significant factor in pregnancy risk. Systolic BP: Systolic blood pressure, indicating the force exerted on artery walls when the heart beats. Elevated levels can indicate hypertension. Diastolic: Diastolic blood pressure, measuring pressure between heartbeats, where high values can be a sign of gestational hypertension or preeclampsia risk. BS (Blood Sugar): Blood sugar level of the patient, crucial for monitoring conditions such as gestational diabetes, which can affect fetal and maternal health. Body Temp: Patient’s body temperature, which can help identify infection or inflammation. BMI (Body Mass Index): A measure of body fat based on height and weight. Higher BMI values can be associated with complications like gestational diabetes and hypertension. Previous Complications: Binary indicator (0 or 1) for previous pregnancy complications, which could predispose patients to future risks. Preexisting Diabetes: Indicates whether the patient has a history of diabetes, an essential factor as it raises the risk for complications. Gestational Diabetes: Presence of diabetes developed during pregnancy, a significant risk factor for both mother and child. Mental Health: Indicator of mental health issues, which may affect pregnancy outcomes and maternal wellbeing. Heart Rate: Heart rate of the patient, which, when elevated, may indicate stress or cardiovascular strain. Risk Level: Categorized risk level (e.g., High, Low), assessing the overall health risk based on the patient’s profile. Applications: This dataset is highly applicable in: Risk Stratification: Helping healthcare providers assess which patients are at higher risk for complications. Predictive Modeling: Facilitating machine learning and statistical models to forecast health risks and inform preventive measures. Maternal Health Research: Supporting studies focused on the impact of various health metrics on pregnancy outcomes. Healthcare Policy: Providing evidence to develop guidelines for maternal healthcare, especially in populations with limited resources. This dataset is an invaluable tool for professionals in obstetrics, public health, and predictive healthcare analytics, aimed at improving the quality of maternal care and optimizing health outcomes for mothers and infants.