Prostate Cancer Prediction Using Machine Learning

Overview

This project applies machine learning techniques to predict and analyze prostate cancer. It integrates feature engineering, data visualization, and advanced classification algorithms to achieve reliable and interpretable results. The primary goal is to assist healthcare professionals in early detection and diagnosis.

Features

• Data Analysis & Visualization: Understand trends and patterns in prostate cancer datasets using tools like seaborn and matplotlib.
• Feature Selection: Automatic identification of the most important predictors using VarianceThreshold and other techniques.
• Deep Learning: Implementation of neural networks with keras for improved prediction accuracy.
• Model Evaluation: Detailed performance analysis using metrics like precision, recall, F1-score, and ROC curves.

Requirements

This project requires Python and the following libraries:

• numpy
• pandas
• seaborn
• matplotlib
• scikit-learn
• keras
• tensorflow

Getting Started

1. Clone the Repository:

   git clone https://github.com/yourusername/prostate-cancer-prediction.git
   cd prostate-cancer-prediction

2. Install Dependencies: Use the package manager pip to install the required libraries:

   pip install -r requirements.txt

3. Dataset:

   • Download the dataset from Prostate Cancer Dataset (replace with the actual link).
   • Place the dataset in the data/ directory.

4. Run the Notebook: Launch the Jupyter Notebook to explore and execute the code:

   jupyter notebook prostate_cancer_using_Machine_learning.ipynb

Project Structure

prostate-cancer-prediction/
│
├── data/
│   └── prostate_cancer.csv          # Dataset
├── models/
│   └── trained_model.h5             # Trained deep learning model
├── notebooks/
│   └── prostate_cancer_analysis.ipynb # Jupyter Notebook
├── images/
│   └── results.png                  # Visualizations and outputs
├── README.md                        # Project documentation
├── requirements.txt                 # Python dependencies
└── utils.py                         # Helper functions

Our Approach for this Research:

Usage

1. Feature Engineering:
   • The notebook automatically applies feature selection and preprocessing techniques.
2. Model Training:
   • Train models using predefined scripts or modify them for custom requirements.
3. Prediction:
   • Input test samples and obtain predictions with confidence scores.

Results

• Model Performance:
  • Accuracy: 95%
  • Precision: 94%
  • Recall: 96%
• Visual Insights:
  • ROC curves, confusion matrices, and feature importance charts are included.

Sample output visualization:

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-new

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-new

5. Open a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For inquiries or issues, please contact:

• Name: Your Name
• Email: [email protected]
• GitHub: S M Mahamudul Hasan