breast-cancer-detection/README.md

🩺 Early Breast Cancer Detection using Blood Biomarkers

Université Paris Dauphine - PSL
Statistical Learning Project — Academic Year 2024–2025
Supervisors: Prof. Gabriel Turinici, Dr. Laetitia Comminges

Project Objective

The goal of this project is to predict the presence of breast cancer using blood-based biomarkers. Several supervised classification models are compared, with a strong focus on recall to reflect the clinical need to minimize false negatives (i.e., undiagnosed patients).

Dataset

• Source: Breast Cancer Coimbra Dataset (116 patients, 9 biomarkers)
• Target variable: Classification (0 = healthy, 1 = cancer)
• Preprocessing steps:
  • Logarithmic transformation of skewed variables
  • Standardization (Z-score)
  • Stratified train/test split (92/24)

Models Compared

Model	Recall	F1-score	AUC
k-Nearest Neighbors (k=23)	0.92	0.788	0.88
Neural Network (MLP)	0.92	0.69	0.83
Logistic Regression (L2)	0.69	0.75	0.79
Gaussian Naïve Bayes	0.58	0.68	0.72

Best model for clinical usage (recall priority): k-NN (k = 23)

Repository Structure

• eda_analysis.ipynb – Data exploration, visualization, and preprocessing
• logistic_regression.ipynb – Logistic regression (basic and optimized via GridSearchCV)
• knn.ipynb – k-Nearest Neighbors with cross-validation and performance tuning
• neural_network.ipynb – Feedforward neural network (MLPClassifier)
• naive_bayes.ipynb – Gaussian Naïve Bayes with log-transformed inputs
• svm.ipynb – Preliminary experiments with SVM (bonus, not included in the final report)
• Subject_3_Ouabdesselam_Forest_Durousseau_Danjou_vonSiemens.pdf – Final report (comprehensive analysis and conclusions)
• README.md – This file

Evaluation Metrics

• Recall: prioritized (to avoid false negatives)
• F1-score: balances precision and recall
• ROC & AUC: overall discriminative ability

Clinical Recommendation

Assuming false positives are acceptable to avoid missing cancer cases, the k-NN (k = 23) model is preferred. It offers the best compromise between recall and F1-score, and reliably identifies patients at risk.

Authors

Erwan Ouabdesselam
Antonin Durousseau
Moritz von Siemens
Arthur Danjou
Thaïs Forest

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Project completed as part of the Statistical Learning course in the Master’s program in Applied Mathematics at Université Paris Dauphine - PSL.