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63 lines
2.6 KiB
Markdown
63 lines
2.6 KiB
Markdown
# 🩺 Early Breast Cancer Detection using Blood Biomarkers
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**Université Paris Dauphine - PSL**
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Statistical Learning Project — Academic Year 2024–2025
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**Supervisors:** Prof. Gabriel Turinici, Dr. Laetitia Comminges
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## Project Objective
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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).
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## Dataset
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- **Source:** Breast Cancer Coimbra Dataset (116 patients, 9 biomarkers)
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- **Target variable:** `Classification` (0 = healthy, 1 = cancer)
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- **Preprocessing steps:**
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- Logarithmic transformation of skewed variables
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- Standardization (Z-score)
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- Stratified train/test split (92/24)
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## Models Compared
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| Model | Recall | F1-score | AUC |
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|-----------------------------|--------|----------|-------|
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| k-Nearest Neighbors (k=23) | 0.92 | 0.788 | 0.88 |
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| Neural Network (MLP) | 0.92 | 0.69 | 0.83 |
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| Logistic Regression (L2) | 0.69 | 0.75 | 0.79 |
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| Gaussian Naïve Bayes | 0.58 | 0.68 | 0.72 |
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**Best model for clinical usage (recall priority):** k-NN (k = 23)
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## Repository Structure
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- `eda_analysis.ipynb` – Data exploration, visualization, and preprocessing
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- `logistic_regression.ipynb` – Logistic regression (basic and optimized via GridSearchCV)
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- `knn.ipynb` – k-Nearest Neighbors with cross-validation and performance tuning
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- `neural_network.ipynb` – Feedforward neural network (MLPClassifier)
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- `naive_bayes.ipynb` – Gaussian Naïve Bayes with log-transformed inputs
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- `svm.ipynb` – Preliminary experiments with SVM (bonus, not included in the final report)
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- `Subject_3_Ouabdesselam_Forest_Durousseau_Danjou_vonSiemens.pdf` – Final report (comprehensive analysis and conclusions)
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- `README.md` – This file
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## Evaluation Metrics
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- **Recall:** prioritized (to avoid false negatives)
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- **F1-score:** balances precision and recall
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- **ROC & AUC:** overall discriminative ability
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## Clinical Recommendation
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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.
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## Authors
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Erwan Ouabdesselam
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Antonin Durousseau
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Moritz von Siemens
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Arthur Danjou
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Thaïs Forest
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---
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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.
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