Spaces:
Sleeping
Sleeping
File size: 2,444 Bytes
7b91feb 55f052e 7b91feb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | ---
title: Diabetes Readmission
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
---
An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
# 🩺 Diabetes Readmission Prediction Web App
An interactive machine learning application deployed on **Hugging Face Spaces** to predict the likelihood of hospital readmission for diabetic patients using clinical and demographic data.
🔗 **Live Space:**
https://huggingface.co/spaces/Parishri07/Diabetes_readmission
---
## 📌 Project Overview
Hospital readmission among diabetic patients is a critical healthcare challenge. This project provides a **web-based prediction system** that helps estimate the probability of readmission using machine learning models.
The application is designed for:
- Educational purposes
- Healthcare analytics demonstrations
- Machine learning deployment practice
---
## ✨ Key Features
- 🔍 **Readmission Prediction**
Predicts whether a diabetic patient is likely to be readmitted.
- 🖥️ **Interactive Web Interface**
Clean and simple UI built using **Gradio**.
- ⚡ **Real-Time Inference**
Instant predictions based on user inputs.
- 🧩 **Modular Codebase**
Easy to extend with new models or features.
---
## 📂 Project Structure
Diabetes_readmission/
├── app.py # Main application file
├── requirements.txt # Python dependencies
├── README.md # Project documentation
├── .python_version # Python version for Hugging Face Space
├── data/
├── models/
└── notebooks/
---
## 🚀 How the App Works
1. User enters patient clinical information through the UI.
2. Input data is processed and passed to a trained ML model.
3. The model predicts readmission risk.
4. Results are displayed instantly on the interface.
---
## 🧠 Machine Learning Approach
The prediction system may use supervised ML models such as:
- Logistic Regression
- Random Forest
- Gradient Boosting / XGBoost
Model performance is evaluated using:
- Accuracy
- Precision
- Recall
- ROC-AUC
---
## 🛠️ Technologies Used
- **Python**
- **Gradio**
- **Scikit-learn**
- **Pandas & NumPy**
- **Hugging Face Spaces**
--- |