🧬 Breast Cancer Detection using Machine Learning (Gene Expression Data)

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📌 Overview

This project focuses on classifying breast cancer samples into tumor and normal categories using gene expression data.
The dataset is obtained from the NCBI Gene Expression Omnibus (GEO) with accession ID GSE183947.

Machine learning techniques are applied to analyze high-dimensional RNA-Seq data and build a predictive classification model.

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🎯 Objective

To develop an accurate and reliable machine learning model for classifying breast cancer samples based on gene expression profiles.

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📊 Dataset Information

• Source: NCBI GEO
• Accession ID: GSE183947
• Data Type: RNA-Seq (FPKM normalized)
• Total Samples: 60
  • Tumor: 30
  • Normal: 30
• Features: ~20,000 genes

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🛠️ Tools and Technologies

• IDE: Visual Studio Code
• Language: Python

📚 Libraries Used

• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• SciPy

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⚙️ Project Workflow

1️⃣ Data Collection

Dataset downloaded from GEO database.

2️⃣ Data Preprocessing

• Dataset transposed (samples → rows, genes → columns)
• Converted to numeric format
• Missing values handled
• Log₂(x + 1) transformation applied
• Feature scaling using StandardScaler

3️⃣ Label Assignment

• Tumor and Normal labels assigned
• Data split into:
  • X (features)
  • y (labels)

4️⃣ Train-Test Split

• 80% training, 20% testing
• Stratified sampling used

5️⃣ Feature Selection

• SelectKBest (ANOVA F-test) used
• Reduced dimensionality

6️⃣ Model Building

• Algorithm: Support Vector Machine (SVM)
• Kernel: Linear

7️⃣ Model Evaluation

• Accuracy Score
• Confusion Matrix
• Classification Report

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📈 Results

• High accuracy achieved in classification
• Feature selection improved performance
• Model generalized well on test data

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📊 Confusion Matrix

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📋 Classification Report

Classification Report: precision recall f1-score support

  normal       1.00      1.00      1.00         6
   tumor       1.00      1.00      1.00         6

accuracy                           1.00        12

macro avg 1.00 1.00 1.00 12 weighted avg 1.00 1.00 1.00 12

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Interpretation

The SVM model achieved 100% accuracy on the test dataset, correctly classifying all tumor and normal samples. However, due to the high dimensionality of gene expression data (~20,000 features) and relatively small sample size (60 samples), the model may be prone to overfitting. Therefore, additional validation techniques such as cross-validation are required to ensure robustness and generalizability of the model.

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📈 ROC Curve

The Receiver Operating Characteristic (ROC) curve was used to evaluate the model’s ability to distinguish between tumor and normal samples.

• AUC Score: 1.0

✅ Results from this Project AUC Score: 1.0

• The ROC curve is positioned near the top-left corner, indicating:
• High sensitivity (correct tumor detection)
• Low false positive rate (minimal misclassification of normal samples)

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📊 PCA Visualization

Principal Component Analysis (PCA) was applied to reduce high-dimensional gene expression data into 2D space for visualization.

🔍 PCA Interpretation

• Clear separation between tumor and normal samples
• Indicates strong feature distinction in dataset
• Supports model’s high accuracy

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Cross Validation

To evaluate the robustness and generalizability of the model, 5-fold cross-validation was performed using a machine learning pipeline that includes feature scaling, feature selection, and classification.

🔧 Pipeline Components

• Feature Scaling: StandardScaler
• Feature Selection: SelectKBest (top 100 genes using ANOVA F-test)
• Classifier: Support Vector Machine (SVM, linear kernel)

🏁 Conclusion

In this project, a machine learning pipeline was developed to classify breast cancer samples using high-dimensional gene expression data. The dataset was preprocessed using log transformation and feature scaling to improve data quality and model performance. Feature selection helped reduce dimensionality and retain the most informative genes.

A Support Vector Machine (SVM) model was trained and evaluated, achieving strong performance in distinguishing tumor and normal samples. The results demonstrate the effectiveness of machine learning techniques in analyzing complex biological data.

This project highlights the potential of computational approaches in bioinformatics for disease classification and can be extended for biomarker discovery and precision medicine applications.

🔮 Future Work

The model can be further validated using independent datasets such as:

• GSE42568 (tumor vs normal classification)
• GSE70947 (RNA-seq based validation)
• GSE2034 (cross-platform validation)

This would help assess the robustness and generalizability of the model across different biological datasets.

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📁 Project Structure

Machine_Learning_Project/ │ ├── data/ │ └── GSE183947_fpkm.csv │ |── images/ | └── Confusion_Matrix_Output_Image.png ├── notebooks/ │ └── Machine_Learning_Project.ipynb │ ├── src/ │ └── model.py │ ├── README.md └── requirements.txt

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▶️ How to Run

git clone https://github.com/joshiyukta16/Machine_Learning_Breast_Cancer_Detection_Model-.git cd Breast-Cancer-Detection-ML pip install -r requirements.txt jupyter notebook

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🧠 Applications

• Breast cancer detection
• Biomarker discovery
• Precision medicine
• Bioinformatics research

🔮 Future Scope

• Apply deep learning models
• Perform feature importance analysis
• Integrate multi-omics data
• Deploy as a web application

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👩‍💻 Author

Yukta Joshi B.Tech Bioinformatics (AI & ML)

🙏 Acknowledgment

Data provided by NCBI GEO database.

⭐ If you like this project, give it a star!!

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