kartikey05 / oncodetect Goto Github PK

Oncodetect: Breast Cancer Prediction Project

🔍 Introduction:

Oncodetect is a predictive modeling project aimed at facilitating early detection of breast cancer. Leveraging machine learning techniques, particularly logistic regression, this project offers a tool to assess the likelihood of breast cancer based on various medical attributes.

📁 Project Structure:

• data.csv: Dataset containing medical attributes of patients, including diagnosis labels.
• model.pkl: Serialized logistic regression model trained on the dataset.

🚀 Getting Started:

1. Clone the Repository:

   git clone https://github.com/kartikey05/oncodetect.git
   cd oncodetect
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Run Prediction:

   python predict.py
   

📊 Model Performance:

• Algorithm Used: Logistic Regression
• Accuracy: 95%
• Precision: 96%
• Recall: 94%
• F1-score: 95%

🔬 Understanding Logistic Regression:

Logistic regression is a statistical method used for binary classification tasks, such as detecting the presence or absence of breast cancer in this project. It models the probability of a binary outcome by applying a sigmoid function to a linear combination of input features and their respective weights.

🎯 Why Oncodetect?

• Early Detection: Enables early detection of breast cancer, potentially improving treatment outcomes and survival rates.
• Accessibility: Provides a user-friendly tool for healthcare professionals to assess breast cancer risk based on patient data.
• Scalability: The logistic regression model scales well with varying dataset sizes and can be easily integrated into existing healthcare systems.

🌟 Contributing:

Contributions to Oncodetect are welcome! Whether you're interested in adding new features, improving model performance, or enhancing documentation, feel free to submit pull requests.

📝 License:

This project is licensed under the MIT License. See the LICENSE file for details.

📧 Contact:

For any inquiries or feedback, please contact [email protected].

👩‍💻 Authors:

-Kartikey Agarwal

📅 Last Updated:

January 1, 2024

🙏 Acknowledgements:

We would like to express our gratitude to the creators of the dataset used in this project and to the open-source community for their valuable contributions.