🔬 Advanced Cancer Prediction System

Model Accuracy: 97.37% | Based on Wisconsin Breast Cancer Dataset

Patient Data Input

📏 Tumor Size Measurements

14.1 mm
Normal: 6-30 mm
91.9 mm
Normal: 40-200 mm
654.9 mm²
Normal: 140-2500 mm²
16.3 mm
Normal: 7-30 mm
107.3 mm
Normal: 50-200 mm
880.6 mm²
Normal: 150-4000 mm²

🔍 Tumor Shape Characteristics

0.10
Normal: 0.0-0.35
0.09
Normal: 0.0-0.5
0.05
Normal: 0.0-0.15
0.25
Normal: 0.0-1.5
0.27
Normal: 0.0-1.2
0.11
Normal: 0.0-0.2

📊 Measurement Errors

0.40 mm
Normal: 0.1-3.0 mm
2.87 mm
Normal: 0.8-25.0 mm
40.3 mm²
Normal: 6-550 mm²

Model Performance & Information

Best Model Performance

Algorithm: Support Vector Machine (SVM)
Accuracy: 97.37%
AUC Score: 99.40%
Cross-Validation: 10-Fold CV

Model Comparison

Performance Metrics

Model Accuracy AUC
SVM 97.37% 99.40%
Neural Network 97.37% 99.64%
Ensemble Voting 96.49% 99.40%
Logistic Regression 95.61% 99.11%
XGBoost 95.61% 99.02%
Random Forest 94.74% 99.08%

Feature Analysis & Importance

Feature Importance

Feature Descriptions

Worst Perimeter

Perimeter of the tumor at worst measurement - most predictive feature

Mean Concave Points

Average number of concave portions of the tumor contour

Worst Radius

Maximum radius measurement from center to perimeter points

Mean Perimeter

Average perimeter measurement of the tumor

Worst Area

Maximum area measurement of the tumor cross-section

Key Insights

📏
Size Matters

Tumor size measurements (radius, perimeter, area) are among the most predictive features

🔍
Shape Characteristics

Concave points and compactness reveal important tumor morphology patterns

📊
Measurement Precision

Error measurements help account for imaging and measurement uncertainties

About This System

🎯 Project Overview

This advanced cancer prediction system uses machine learning algorithms to analyze tumor characteristics and predict the likelihood of malignancy. The system is trained on the Wisconsin Breast Cancer Dataset and employs state-of-the-art Support Vector Machine algorithms.

📊 Dataset Information

Dataset: Wisconsin Breast Cancer Dataset
Features: 30 real-valued features computed from digitized images
Samples: 569 instances
Classes: Malignant (212) and Benign (357)
Source: UCI Machine Learning Repository

⚙️ Technical Specifications

Algorithm: Support Vector Machine (RBF Kernel)
Feature Selection: Top 15 most important features
Data Preprocessing: StandardScaler normalization
Validation: 10-Fold Cross Validation
Performance: 97.37% accuracy, 99.40% AUC

⚠️ Medical Disclaimer

Important: This system is designed for educational and research purposes only.

  • This tool should NOT be used as a substitute for professional medical diagnosis
  • All medical decisions should be made in consultation with qualified healthcare professionals
  • The predictions are based on statistical models and may not account for all clinical factors
  • Regular medical check-ups and professional screening remain essential

🚀 Future Enhancements

🤖
Deep Learning Integration

Implement CNN-based image analysis for direct tumor imaging

📱
Mobile Application

Develop mobile app for real-time predictions and monitoring

🔬
Multi-Cancer Detection

Extend to other cancer types with specialized models