Bandile Malaza

Overview

An intelligent system that combines predictive analytics, LLMs, and RAG to assess diabetes risk, offer lifestyle recommendations, and provide context-aware support using Eswatini health data.
🧠 Powered by explainable AI for smarter, localized health decisions.
This project focuses on leveraging machine learning algorithms to predict and manage diabetes and heart disease. The main objective is to develop a web application that uses predictive models to identify high-risk individuals and provide personalized recommendations for preventive care. The project aims to improve healthcare accessibility and empower healthcare providers to take a proactive approach to managing non-communicable diseases. The project includes the development of a user-friendly web application that allows users to input relevant health data for predicting the risk of diabetes and heart disease. The application provides personalized risk percentages, clear risk category labels, and lifestyle modification recommendations based on the predictions.

Technologies Used

• Flask
• Machine learning (ML)
• Large Language Models (LLM)
• Retrival Augmented Generation (RAG)