Data-Driven Prediction of Maternal Health Risk Level Using Machine Learning Algorithms

The project aims to predict maternal health risk levels using machine learning techniques on temporal data. The dataset used includes maternal health parameters such as age, systolic blood pressure, diastolic blood pressure, blood glucose levels, body temperature, and heart rate, all of which are critical indicators of maternal health. The goal is to predict the risk level of a pregnant woman based on these factors using advanced machine learning models. The existing models like Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes, Gradient Boosting, and AdaBoost are commonly used in predicting health outcomes, but this project proposes to enhance prediction accuracy using advanced algorithms like  NeuroBoost Synergy, XGBoost, and Stacking Classifiers. The front-end of the system is developed using HTML, CSS, and JavaScript, while the backend is implemented using Python with Flask, and the system's data is stored in a MySQL database. This project contributes to improving the prediction of maternal health risks, potentially aiding healthcare providers in taking preventive measures for at-risk pregnancies.

Key words:

Maternal Health, Risk Prediction, Machine Learning,  NeuroBoost Synergy, XGBoost, Stacking Classifier, Logistic Regression, Decision Trees, Pregnancy Health, Temporal Data, Blood Pressure, Blood Glucose Levels, Healthcare Technology.