--- title: Early-prediction-for-ml Proj emoji: 📉 colorFrom: blue colorTo: green sdk: streamlit sdk_version: 1.44.0 app_file: app.py pinned: false short_description: This tool provides early prediction and analysis for various --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference --- # 🩺 Early Prediction of Health & Lifestyle Diseases Welcome to the **AI-Powered Health Prediction System**! 🚀 This tool provides **early prediction and analysis** for various health conditions using **Machine Learning & NLP**. It assists users in understanding potential health risks based on their lifestyle, medical indicators, and symptoms. --- ## 🏥 Available Features: ✅ **Diabetes Prediction** – Predict diabetes risk using medical indicators. ✅ **Hypertension Prediction** – Assess the risk of high blood pressure. ✅ **Cardiovascular Disease Prediction** – XGBoost-based prediction for heart disease. ✅ **Stroke Prediction** – Machine Learning-based stroke risk analysis. ✅ **Asthma Prediction** – Detect asthma likelihood using preprocessed datasets. ✅ **Sleep Health Analysis** – AI-driven analysis of sleep patterns and health. ✅ **Mental Health Assessment** – Sentiment-based analysis using **mental-roberta-base**. ✅ **Medical Consultant AI Chatbot** – Ask health-related questions for AI-driven insights. ✅ **Data Visualization** – Graphical representation of health trends and anomalies. 👉 **Select an option from the sidebar to proceed!** --- ## 🚀 Quick Start Guide 1. Clone this repository: ```bash git clone https://github.com/MOHITRAJDEO12345/early-prediction-for-ml_proj.git ``` 2. Navigate to the project directory: ```bash cd early-prediction-for-ml_proj ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Run the application: ```bash streamlit run app.py ``` --- ## 🖥 Application Sections The application includes the following navigation options: ```python options = [ 'Home', 'Diabetes Prediction', 'Hypertension Prediction', 'Cardiovascular Disease Prediction', 'Stroke Prediction', 'Asthma Prediction', 'Sleep Health Analysis', 'Mental-Analysis', 'Medical Consultant', 'Data Visualization' ] ``` ### 🧠 Mental Health Analysis - Uses **mental/mental-roberta-base** for sentiment-based mental health assessment. - Predicts **Depression and Anxiety** based on user input. - Provides graphical risk assessment using **Seaborn & Matplotlib**. ### 🔬 Disease Prediction Models - **Diabetes Model**: Predicts diabetes risk based on medical data. - **Hypertension Model**: Evaluates high blood pressure risk. - **Cardiovascular Model**: Uses XGBoost for heart disease prediction. - **Stroke Model**: ML-based assessment of stroke risk factors. - **Asthma Model**: Machine learning model for asthma detection. ### 📊 Data Visualization - Interactive graphs to analyze health trends. - Anomaly detection for user predictions. ### 🤖 AI Medical Consultant - AI-powered chatbot for answering health-related queries. - Uses NLP models for better understanding and recommendations. --- ## 📸 Screenshots & UI Preview 🔍 **Streamlit Application Interface:** ![Streamlit UI](screenshots/streamlit.png) 📊 **Data Visualization Example:** ![Data Visualization](screenshots/data_visual.png) ![User Graph](screenshots/user_graph.png) 🖥 **Separate Frontend Interface:** ![Frontend UI](screenshots/frontend.png) --- ## ⚠️ Disclaimer This application has been developed using real-world healthcare datasets sourced from Kaggle: - **Diabetes Dataset** - **Hypertension Dataset** - **Cardiovascular Disease Dataset** - **Stroke Prediction Dataset** - **Asthma Analysis & Prediction Dataset** - **Sentiment Analysis for Mental Health** The predictions are generated using machine learning models trained on these datasets, incorporating evaluation metrics and graphical insights to enhance interpretability. However, this tool has **not undergone clinical validation** and should be used for **informational and educational purposes only**. It is not intended to serve as a substitute for **professional medical diagnosis or treatment**. Always consult a qualified healthcare provider for medical advice. --- # 🔬 Ongoing Research & Future Enhancements 🚧 **Fitbit API Integration** – Real-time health monitoring with wearable devices. 🚧 **LSTM Models for Realtime Fitbit Data** – Developing deep learning models for dynamic health tracking. 🚧 **Enhanced Mental Health Analysis** – Exploring transformer-based sentiment models for deeper insights. 🚧 **Hybrid ML & NLP Systems** – Combining structured health data with unstructured text for more accurate predictions. --- ## 👨‍💻 Author Developed by **Mohit Rajdeo** GitHub: [MOHITRAJDEO12345](https://github.com/MOHITRAJDEO12345) --- ## 🤝 Contributions Contributions are always welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. --- ## 📬 Contact For any questions or feedback, feel free to reach out: 📧 Email: mohitrajdeo16deoghar@gmail.com 🐦 Twitter: [@mohitrajdeo](https://twitter.com/mohitrajdeo)