https://github.com/afrinaf/cancerpredictorapplication
CancerPredictorApplication predicts cancer likelihood using machine learning models based on patient data.
https://github.com/afrinaf/cancerpredictorapplication
flask-application machine-learning-algorithms prediction-model python
Last synced: 5 months ago
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CancerPredictorApplication predicts cancer likelihood using machine learning models based on patient data.
- Host: GitHub
- URL: https://github.com/afrinaf/cancerpredictorapplication
- Owner: Afrinaf
- Created: 2022-10-15T06:03:29.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-13T17:46:09.000Z (9 months ago)
- Last Synced: 2025-01-09T08:23:10.213Z (6 months ago)
- Topics: flask-application, machine-learning-algorithms, prediction-model, python
- Language: HTML
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CancerPredictorApplication
## Overview
CancerPredictorApplication is a machine learning-based web application designed to predict the likelihood of breast cancer based on medical data. Using a trained decision tree classifier, the application takes input from the user and provides a prediction indicating whether or not the individual is at risk of breast cancer.
## Features
- Predicts breast cancer likelihood using a decision tree classifier.
- User-friendly web interface for inputting medical data.
- Instant feedback on cancer risk after data submission.
## Technologies Used
- **Python**: Core programming language.
- **Flask**: Web framework used to build the web interface.
- **Pandas**: For data manipulation and reading CSV files.
- **Scikit-learn**: For machine learning (Decision Tree Classifier).
- **HTML**: To create the web interface.
## Dataset
The application uses a dataset (`dataR2.csv`) containing medical features such as:
- Age
- BMI (Body Mass Index)
- Glucose
- Insulin
- HOMA
- Leptin
- Adiponectin
- Resistin
- MCP.1
- Classification (target variable)
The model is trained on this data to predict the classification, which indicates the presence or absence of breast cancer.
## How to Use
1. Clone the repository:
```bash
git clone https://github.com/yourusername/CancerPredictorApplication.git
cd CancerPredictorApplication
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Place the dataset (`dataR2.csv`) in the root folder.
4. Run the Flask application:
```bash
python app.py
```
5. Open your browser and navigate to `http://127.0.0.1:5000/`.
6. Fill in the form with the required medical data and submit to see the prediction.
## Output
## Project Structure
```
CancerPredictorApplication/
│
├── templates/
│ └── index.html # HTML file for the web interface
├── app.py # Main Flask application file
├── dataR2.csv # Medical dataset used for training
├── requirements.txt # Python dependencies
└── README.md # This README file
```
## Future Enhancements
- Improve the model by testing other algorithms (e.g., SVM, Naive Bayes).
- Add data validation and error handling for form inputs.
- Use a database to store user inputs and prediction results.
## License
This project is open-source and available under the MIT License.