📌 Project Overview
This AI-based Medical Diagnosis Application is a student project developed at Universitas Amikom Yogyakarta. It leverages Machine Learning (ML) models to predict diseases based on user input data and provides informative data visualizations.
💾 Repository Contents
This repository includes:
- Preprocessed medical datasets
- Google Colab Notebooks for model training
- Trained model files (.pkl) for disease prediction:
- Breast Cancer
- Diabetes
- Fetal Health
- Heart Disease
- Kidney Disease
- Lung Cancer
- Streamlit web application code
🚀 Key Features
- 🏥 AI-based disease prediction using Machine Learning models
- 📊 Interactive Data Visualization with Matplotlib & Seaborn
- 🌐 User-friendly Web Interface built with Streamlit
- 🔍 Secure Data Handling using Pandas
- 📱 Responsive design accessible from various devices
🛠️ Technologies Used
- Python 🐍 - Primary programming language
- Streamlit 🎨 - Framework for building web apps
- Scikit-Learn 🤖 - Machine Learning library
- Pandas & NumPy 📊 - Data manipulation and analysis
- Matplotlib & Seaborn 📈 - Data visualization libraries
- Pickle 💾 - For saving and loading trained models
📋 System Requirements
- Python 3.7 or newer
- Minimum 4GB RAM (8GB recommended)
- At least 1GB of storage space
📌 How to Run the Application Locally
Step 1: Clone the Repository
git clone https://github.com/0073212/AI_Medical_Diagnosis_App.git
cd AI_Medical_Diagnosis_App
Step 2: Create and Activate Virtual Environment
# Create virtual environment
python -m venv venv
# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On Mac/Linux:
source venv/bin/activate
Step 3: Install Dependencies
pip install -r requirements.txt
Step 4: Run the Application
streamlit run app.py
The app will open in your browser at http://localhost:8501
AI_Medical_Diagnosis_App/
├── app.py # Main Streamlit app file
├── requirements.txt # Python dependencies
├── models/ # Folder containing trained models (.pkl)
│ ├── breast_cancer_model.pkl
│ ├── diabetes_model.pkl
│ ├── fetal_health_model.pkl
│ ├── heart_disease_model.pkl
│ ├── kidney_disease_model.pkl
│ └── lung_cancer_model.pkl
├── data/ # Folder with datasets
├── notebooks/ # Google Colab notebooks
└── README.md # Project documentation
📊 Available Models
| Disease | Accuracy | Algorithm | Status |
|---|---|---|---|
| Breast Cancer | 95%+ | Random Forest | ✅ Active |
| Diabetes | 90%+ | SVM | ✅ Active |
| Fetal Health | 92%+ | Decision Tree | ✅ Active |
| Heart Disease | 88%+ | Logistic Regression | ✅ Active |
| Kidney Disease | 94%+ | Random Forest | ✅ Active |
| Lung Cancer | 91%+ | XGBoost | ✅ Active |
🚨 Disclaimer
⚠️ IMPORTANT:
This application is intended for educational and research purposes only.
The predictions generated should not be used as a substitute for professional medical advice.
Always consult a healthcare provider for diagnosis and treatment.
🤝 Contributions
We welcome contributions from the community!
- Fork this repository
- Create a new branch (git checkout -b feature/AmazingFeature)
- Commit your changes (git commit -m ‘Add some AmazingFeature’)
- Push to the branch (git push origin feature/AmazingFeature)
- Open a Pull Request
Made with ❤️ by a student of Universitas Amikom Yogyakarta