Breast Cancer Detection

📋 Understanding the Measurements

What are these measurements? These values come from analyzing cell nuclei images from breast tissue samples. Each measurement describes different aspects of the cell's size, shape, and texture.

📊 Understanding the Results:

Benign (B): Non-cancerous - The cells appear normal and healthy. This is good news! The tissue is not cancerous.
Malignant (M): Cancerous - The cells show abnormalities that may indicate cancer. This requires medical attention and further testing by a doctor.

⚠️ Important Medical Disclaimer: This tool is for educational purposes only. The results are predictions based on machine learning and should NOT replace professional medical diagnosis. Always consult with a qualified healthcare provider for accurate diagnosis and treatment decisions.

How to use:

Hover over the ? icon next to any field to see what it means
Use "Load Random Example" to see sample data
Enter your diagnostic measurements and click "Get Prediction"

Terminology Guide:

Mean = Average value across all cells
SE (Standard Error) = How much the values vary
Worst = Most abnormal/severe value found

Mean Features (Average measurements across all cells)

Radius Mean Average radius of cell nuclei

Texture Mean Variation in cell appearance

Perimeter Mean Average perimeter of cell nuclei

Area Mean Average area of cell nuclei

Smoothness Mean Smoothness of cell boundaries

Compactness Mean How compact/circular the cell shape is

Concavity Mean Severity of indentations in the cell

Concave Points Mean Number of indented points in the cell

Symmetry Mean How symmetrical the cell shape is

Fractal Dimension Mean Complexity of the cell boundary

Standard Error Features (Variation/uncertainty in measurements)

Radius SE Variation in radius measurements

Texture SE Variation in texture measurements

Perimeter SE Variation in perimeter measurements

Area SE Variation in area measurements

Smoothness SE Variation in smoothness measurements

Compactness SE Variation in compactness measurements

Concavity SE Variation in concavity measurements

Concave Points SE Variation in concave points measurements

Symmetry SE Variation in symmetry measurements

Fractal Dimension SE Variation in fractal dimension measurements

Worst Features (Most abnormal/severe measurements)

Radius Worst Largest radius measurement

Texture Worst Highest texture variation

Perimeter Worst Largest perimeter measurement

Area Worst Largest area measurement

Smoothness Worst Least smooth boundary found

Compactness Worst Least compact shape found

Concavity Worst Most severe indentations found

Concave Points Worst Maximum indentations found

Symmetry Worst Least symmetrical cell found

Fractal Dimension Worst Most complex boundary found

Processing your data...

Prediction Result

🏥 Breast Cancer Detection

📋 Understanding the Measurements

Mean Features (Average measurements across all cells)

Standard Error Features (Variation/uncertainty in measurements)

Worst Features (Most abnormal/severe measurements)