Maternal Health Analysis

Abstract

Maternal health is a critical aspect of healthcare that directly affects the well-being of both the mother and the developing child. Early identification of pregnancy-related risks such as hypertension, gestational diabetes, anemia, and high blood pressure is essential to reduce maternal mortality and ensure safe pregnancies. Traditional maternal health monitoring methods rely on periodic medical checkups and manual analysis, which may not always provide timely and accurate risk assessment. To address this challenge, this project proposes a machine learning-based maternal health risk prediction system that assists healthcare professionals in identifying potential complications at an early stage. The proposed system analyzes key medical parameters such as age, blood pressure, blood glucose level, and other health indicators to classify pregnant women into low-, medium-, and high-risk categories. A Random Forest Classifier is used to learn patterns from maternal health datasets and generate accurate predictions. The system also provides probability scores to indicate the confidence level of each prediction and is integrated into a user-friendly web application for real-time access and easy clinical use. The developed solution aims to improve early diagnosis, support clinical decision-making, reduce pregnancy-related complications, and enhance maternal and fetal health outcomes through intelligent and data-driven healthcare technology.