New Jersey Maternal Mortality (NJMM) is an interactive web application that visualizes maternal mortality in the state of New Jersey in order to raise awareness of maternal mortality and make it easier to see how it relates to other potential factors (e.g. access to healthcare).

Mama's Hub web application used for administration and data collection.

The notebook aims to examine various factors that impact the health of pregnant women and develop a machine learning model to predict the level of health risk for pregnant women.

A maternal health platform empowering ASHA workers with smart data tools to detect high-risk pregnancies, deliver personalized dietary advice, and ensure safer outcomes in rural India.

A repository of CDC National Center for Health Statistics maternal mortality data and a selection of state data, from Michigan, Minnesota and North Carolina, about maternal deaths

This repository contains python programs for data predictions using machine learning algorithms

This project focuses on developing an AI-powered system to predict and prevent pre-eclampsia in women aged 15–45. Pre-eclampsia is a serious pregnancy complication characterized by high blood pressure and potential damage to organs, such as liver and kidneys. Early identification is crucial to improving maternal and neonatal health outcomes.

An intelligent maternal healthcare assistant for expecting mothers, offering personalized tracking, guidance, and support throughout pregnancy and postpartum.

TCC apresentado como requisito parcial para conclusão do curso de Bacharelado em Estatística pela UFES.

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This is the official repository for NeoMama App.

Lightweight AI assistant for maternal health guidance using ZIP-code data, retrieval-augmented generation, and Colab deployment.

Dissertação do Dr. Fabiano Elisei Serra.

Machine learning-based fetal health classification system using cardiotocography data. Compares multiple algorithms (XGBoost, Random Forest, MLP, Logistic Regression) for predicting fetal health status with 95.9% accuracy.

Statistical analysis of birthweight and related predictors using Python

🤰 AI-powered maternal health companion for Ethiopian mothers. Features Amharic voice assistant, symptom checker, pregnancy tracking, and offline-capable health guidance. Built with Flutter & NestJS.

Beginner-level data analysis on birthweight and maternal smoking using Python

This project applies supervised machine learning models to predict postpartum depression (PPD) using a Kaggle survey dataset. Three models were implemented: Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM).

Pregnancy Coach with Nutrition, Reminders and Refferals.