Breast Cancer Detection Using Machine Learning

This project uses machine learning techniques to predict whether a breast tumor is benign or malignant, offering healthcare providers a tool for faster and more accurate diagnoses. The project utilizes the Wisconsin Breast Cancer Dataset and evaluates the performance of three different models, with the Support Vector Machine (SVM) model achieving an impressive accuracy of 97.80%.

Goal

The primary objective is to:

• Automate early detection of breast cancer.
• Improve diagnostic speed.
• Reduce false positives.
• Lower healthcare costs.

Early and accurate identification of malignant tumors can lead to more efficient treatment, ultimately saving lives.

Technologies Used

• Python
• scikit-learn
• pandas
• Matplotlib

Key Models

• Logistic Regression
• Random Forest
• Support Vector Machine (SVM) (Achieved 97.80% accuracy)

Each model was evaluated and compared based on key performance metrics such as:

• Accuracy
• Precision
• Recall
• F1-Score

Explore the Full Project on GitHub

For the complete code, Jupyter Notebooks, and detailed documentation, visit the project repository on GitHub.

Feel free to fork the repository and contribute to the project! I welcome any improvements or suggestions.

Contact

If you have any questions or would like to collaborate, don’t hesitate to reach out via email: services@isaacgyane.com.

Suggestions for Enhancements in Gutenberg

1. Add an Image Block: Include a chart or diagram that illustrates the model’s performance metrics.
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2. Use a List Block for Features:
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3. Embed a GitHub Block:
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4. Add a Video Block: If you have a YouTube link that demonstrates the project, embed it using the YouTube block.
5. Star Rating Block (Optional):
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This structure ensures your project description is easy to read and visually engaging when displayed on your WordPress page.