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🧬 Liver Cancer Gene Expression Classification

This repository contains a complete machine learning pipeline for classifying liver cancer based on gene expression data. It includes data preprocessing, exploratory analysis, dimensionality reduction using PCA, and classification using various ML models.

📁 Dataset

The dataset used in this project is assumed to be in CSV format and named liver_cancer_expression_data.csv, containing gene expression features and a type column indicating class labels (e.g., HCC vs. normal).

🛠️ Features of the Code

  • Missing value handling with column-wise mean imputation.
  • Feature normalization using z-score standardization.
  • Dimensionality reduction using Principal Component Analysis (PCA).
  • Visualizations for data distribution and PCA projection.
  • ML models: Random Forest, SVM, XGBoost, Neural Network (MLP).
  • Hyperparameter tuning using GridSearchCV.
  • Evaluation using accuracy, classification report, confusion matrix, and AUC-ROC.

🔧 Requirements

Install the following Python packages before running the script:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost

🧪 How to Run

Place your dataset file as liver_cancer_expression_data.csv in the project directory.

Run the script using:

python liver_cancer_classification.py

🧹 Data Preprocessing

  • Loaded dataset and checked for null values.
  • Imputed missing values with mean (for numeric columns).
  • Standardized features using StandardScaler.
  • Visualized 20 randomly selected standardized features using boxplots.

📊 PCA Visualization

  • Reduced data to 2D using PCA for visualization.
  • Plotted principal components with color-coded classes.

⚙️ Machine Learning Models

Trained and evaluated the following models:

Model Hyperparameters Tuned Evaluation Metrics
Random Forest n_estimators, max_depth, min_samples_split Accuracy, AUC-ROC
SVM C, kernel Accuracy, AUC-ROC
XGBoost n_estimators, max_depth Accuracy, AUC-ROC
Neural Network alpha (regularization) Accuracy, AUC-ROC

Note: Dimensionality reduction to 50 principal components was performed before training to avoid overfitting.

📈 Model Evaluation

  • Accuracy and classification reports printed for each model.
  • ROC curves plotted with AUC scores for probabilistic models.

📌 Example Output (Console)

Random Forest Test Accuracy: 0.8750

📊 Classification Report

Class Precision Recall F1-score Support
Normal 0.89 0.86 0.87 14
HCC 0.86 0.89 0.88 14

AUC-ROC Score for Random Forest: 0.93

📎 Notes

  • Set random seed for reproducibility.
  • Suitable for high-dimensional datasets with limited samples.
  • Uses stratify=y in train-test split to preserve class balance.

🧠 Author

Divya Karade
Chemoinformatics & ML in Drug Discovery
LinkedIn | GitHub

📄 License

This project is open-source and available under the MIT License.