Support Vector Machine-based Breast Cancer Detection: A Complete Practice of Feature Engineering and Model Optimization

This article introduces a machine learning project for breast cancer detection using Support Vector Machines (SVM). Through rigorous feature selection and hyperparameter optimization, it achieved an accuracy of 98.68% on the Wisconsin Breast Cancer Dataset. The project demonstrates a complete data science workflow and provides a valuable reference case for medical AI applications.

Breast cancer is one of the most common malignant tumors among women worldwide, and early diagnosis is crucial for improving patient survival rates. Traditional diagnosis relies on pathologists' experience, while machine learning technology provides new possibilities for auxiliary diagnosis. The project uses the Wisconsin Breast Cancer Dataset, which contains 569 samples and 30 morphological features (each feature includes mean, standard deviation, and worst value), with the target variable being a binary classification of malignant/benign.

The project uses multi-dimensional statistical methods to select features: 1. Correlation analysis to eliminate highly collinear variables; 2. Evaluate the distribution overlap between malignant and benign samples to exclude features with low discriminative power; 3. Calculate the correlation between features and the target variable to retain strongly correlated features; 4. Visualize pairwise features to observe distribution patterns.

Reasons for choosing SVM: Excellent performance in high-dimensional spaces, suitable for medical data with small sample sizes and high feature dimensions, and maximum margin classification improves generalization ability. Steps: 1. Standardize features using StandardScaler; 2. Optimize hyperparameters (C, kernel function, gamma) via GridSearchCV (5-fold cross-validation).

Performance of the optimized SVM model on the test set: Accuracy of 98.68%, F1 score of approximately 1.0, recall of approximately 1.0. The confusion matrix shows an extremely low false negative rate, which is crucial in medical diagnosis (false negatives have more severe consequences).

The project is based on the Python ecosystem: Pandas (data processing), NumPy (numerical computation), Scikit-learn (models, feature scaling, cross-validation), Matplotlib/Seaborn (visualization), and Jupyter Notebook (development and documentation), ensuring code reproducibility.

Project insights: 1. Feature engineering takes priority over model tuning; 2. Statistical thinking guides machine learning; 3. Medical scenarios require attention to error types (e.g., false negatives). The project provides a reference template for medical AI learners, and open-source code and documentation help community development and promote the progress of AI-assisted diagnosis technology.