Maternal Health Analysis
About Project
The Maternal Health Project aims to analyze and interpret key clinical and observational data to better understand maternal well-being during pregnancy. The dataset includes vital health indicators such as medical history, nutritional assessments, and ultrasound measurements. Through structured data engineering, transformation, and analysis, the project delivers meaningful insights to improve maternal and child healthcare outcomes.
Clinical and diagnostic observations were collected from expectant mothers and processed using Python for data ingestion and cleaning, PostgreSQL for data modeling and transformation, and Power BI for visualization and insight generation. The data engineering process involved developing staging and logical schemas, implementing DDL and DML operations, and applying transformation logic for structured analysis.
Exploratory and advanced analytics were then performed to uncover trends, relationships, and potential risk factors affecting maternal health. The overall goal of the project is to identify early indicators, enable preventive healthcare measures, and support professionals in designing better maternal care plans that promote safe and healthy pregnancies.
Summary
The analysis revealed significant links between BMI, nutrition, lifestyle choices, and pregnancy risks. High BMI, excessive visceral fat, and poor eating habits were associated with higher rates of cesarean delivery, gestational diabetes, miscarriage, and neonatal complications. Conversely, healthier eating and lifestyle behaviors correlated with improved maternal and fetal outcomes.
Power BI dashboards enabled clear visualization of risk factors, showing that early identification and continuous monitoring can significantly reduce complications. The project demonstrated how data-driven healthcare insights can guide better prenatal care, improve outcomes, and support long-term maternal health.
Recommendations
Strengthen Early Risk Identification
Include BMI, waist-hip ratio, visceral fat, and lifestyle screening at the first antenatal visit.
Use dashboards and EMR alerts for real-time detection.
Impact: +20% earlier high-risk pregnancy detection, -15% emergency C-section rates.
Personalized Lifestyle Interventions
Tailor nutrition and physical activity programs for high-BMI mothers.
Provide counseling, trimester-specific plans, and community support.
Impact: -10–15% gestational diabetes cases within 12 months.
Continuous Monitoring & Feedback
Track patient progress through dashboards and mobile apps.
Enable multidisciplinary monthly reviews and rapid response.
Impact: +25% faster interventions, +20% Apgar score improvements.
Lifestyle Risk Mitigation
Reduce smoking, alcohol, and drug use through education and support.
Integrate lifestyle counseling in early pregnancy care.
Impact: Up to -25% miscarriage risk in high-BMI groups, healthier long-term outcomes.