OpenHealth

Inspiration

Healthcare in many regions lacks easy access to early diagnosis and personalized medical guidance. We were inspired to build OpenHealth, an AI-powered system that helps detect multiple diseases and provide intelligent health insights using modern machine learning and AI technologies.

What it does

OpenHealth is a multi-disease detection platform that predicts diseases related to organs like the brain, heart, lungs, liver, and kidney. It uses machine learning and deep learning models to analyze medical data and provides personalized health insights and diet recommendations using AI.

How we built it

We built OpenHealth using Python, Flask, and Streamlit with machine learning models such as Random Forest, Gradient Boosting, VGG19, and ResNet50. We integrated LLMs from Hugging Face and Google Generative AI for health insights. The system also uses MLflow, DVC, and Docker for experiment tracking, data versioning, and deployment.

Challenges we ran into

One major challenge was combining multiple disease models into a single scalable platform. Managing datasets, tracking experiments, and integrating large language models while ensuring reliable outputs required careful system design.

Accomplishments that we're proud of

We successfully developed a modular AI healthcare platform capable of multi-disease prediction with integrated AI health assistance and diet recommendations. Implementing MLOps tools also made the system more scalable and reproducible.

What we learned

This project helped us understand how to combine machine learning, deep learning, and generative AI into a real-world healthcare solution. We also gained experience with MLOps practices, model evaluation, and scalable AI system design.

What's next for OpenHealth

In the future, we plan to add more disease prediction models, wearable health data integration, cloud deployment, and telemedicine support to make OpenHealth a more comprehensive remote healthcare platform.

Built With

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• and-seaborn.-we-integrated-large-language-models-from-hugging-face-and-google-generative-ai-(gemini-api)-to-generate-personalized-health-insights.-for-mlops-and-experiment-management
• architecture
• data-version-control-(dvc)
• deployment
• docker.
• efficient
• for
• gradient-boosting
• healthcare
• matplotlib
• modular
• numpy
• pandas
• scalable
• supports
• system
• the
• vgg-19
• we-implemented-machine-learning-and-deep-learning-models-such-as-random-forest
• we-used-mlflow
• we-used-python-as-the-primary-programming-language-for-machine-learning-and-backend-development.-the-platform-was-built-using-flask-and-streamlit-for-web-interfaces-and-api-services.-for-disease-prediction