The Maternal Health Risk Predictor is a machine learning-based web application that predicts potential health risks during pregnancy. The model evaluates key health indicators such as age, blood pressure, blood sugar levels, body temperature, and heart rate to assess maternal health risks. Based on the prediction, the application generates a personalized prescription to help manage and mitigate health risks.
- Risk Prediction: Uses a machine learning model to predict health risks based on various maternal health parameters such as age, systolic and diastolic blood pressure, blood sugar, body temperature, and heart rate.
- Personalized Prescription: Provides a prescription or recommendation based on the predicted risk level. The higher the risk, the more specific the advice given to improve maternal health.
- User-Friendly Interface: The application provides an intuitive web interface where users can easily input health data and receive predictions without complex steps.
- Back-end Model: The backend is built using Python and Flask, with a Random Forest algorithm to process the data and predict the health risk accurately.
- Front-end: Simple and responsive web design built with HTML5, CSS3, and JavaScript to ensure smooth interaction for the user.
- Programming Language: Python
- Web Framework: Flask
- Machine Learning Algorithm: Random Forest
- Frontend: HTML, CSS, JavaScript
Clone the repository to your local machine using the following command:
git clone https://github.com/sunnychaudhary0722/Maternal-Health-Risk-Predictor.git