MaternaguardAi

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  overview

MaternaGuard AI was inspired by the urgent need to reduce preventable maternal health risks, especially in underserved communities. In many regions, pregnant women lack early risk assessment tools that could help detect complications before they become life-threatening. We wanted to build an accessible AI-powered solution that empowers healthcare providers and communities with early insights, supporting safer pregnancies and informed medical decisions.

MaternaGuard AI is a machine learning-powered web application that predicts potential maternal health risks based on key medical indicators.

Users input relevant health data, and the system analyzes it using a trained predictive model to assess risk levels. The application then provides an immediate risk evaluation, helping users and healthcare workers make proactive decisions. The app is deployed online, making it accessible from anywhere without installation.

We built MaternaGuard AI using:

Python for data processing and model development Scikit-learn for training the machine learning classification model Pandas & NumPy for data preprocessing and feature handling Streamlit to develop an interactive and user-friendly web interface Render for cloud deployment Git & GitHub for version control and collaboration The workflow included: Data cleaning and preprocessing Exploratory data analysis Model training and evaluation Model serialization (.pkl file) Building the Streamlit frontend Deploying the application to the cloud

Ensuring proper preprocessing alignment between training and prediction stages

Handling model serialization and loading errors Configuring deployment settings correctly for cloud hosting Managing environment dependencies with a clean requirements.txt Fixing port configuration issues during deployment Each challenge strengthened our understanding of machine learning pipelines and real-world deployment practices.

Successfully training and deploying a working machine learning model

Building a functional and interactive web application Deploying the solution live on Render Guiding students through the end-to-end AI development lifecycle Turning a health-focused idea into a tangible, accessible tool

Through this project, we learned:

The importance of clean, consistent data preprocessing How to move from a local ML project to a production-ready deployment The realities of cloud deployment and environment management The value of mentorship and collaborative problem-solving How AI can be applied to solve meaningful healthcare challenges

Improve model accuracy with more diverse datasets

Add explainable AI features to show why a prediction was made Enhance the user interface and user experience Integrate real-time health data collection Partner with healthcare organizations for pilot testing MaternaGuard AI is just the beginning. The long-term vision is to expand it into a comprehensive maternal health decision-support system that contributes to reducing preventable maternal risks globally.

Built With

• django
• python
• sqlite