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https://github.com/hasansust32/prostate_cancer_predictio
His study addresses these concerns by predicting prostate cancer using six (6) machine learningtechniques: Random Forest, SVM, KNN, Logistic Regression, Neutral Network, and the Ensemble model. We gathered data from 100 patients who were placed in ten different circumstances. The data was categorised as malignant or non-cancerous. Among the six machine learning techniques, logistic regression, neuralnetworks, and ensemble learning have the potential to reach an accuracy of 95.00 percent. Ensemble learning can detect 96.55%of true positive prostate cancer in our model. KNN has a 90%accuracy rate, whereas SVM and Random Forest have an 85%accuracy rate.
https://github.com/hasansust32/prostate_cancer_predictio
cancer-detection cancer-research healthcare machinelearning-python prostate-cancer prostate-cancer-biopsies prostate-cancer-detection
Last synced: about 1 month ago
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His study addresses these concerns by predicting prostate cancer using six (6) machine learningtechniques: Random Forest, SVM, KNN, Logistic Regression, Neutral Network, and the Ensemble model. We gathered data from 100 patients who were placed in ten different circumstances. The data was categorised as malignant or non-cancerous. Among the six machine learning techniques, logistic regression, neuralnetworks, and ensemble learning have the potential to reach an accuracy of 95.00 percent. Ensemble learning can detect 96.55%of true positive prostate cancer in our model. KNN has a 90%accuracy rate, whereas SVM and Random Forest have an 85%accuracy rate.
- Host: GitHub
- URL: https://github.com/hasansust32/prostate_cancer_predictio
- Owner: hasansust32
- Created: 2021-07-24T14:29:54.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-25T00:00:28.000Z (about 1 month ago)
- Last Synced: 2024-11-25T00:25:23.751Z (about 1 month ago)
- Topics: cancer-detection, cancer-research, healthcare, machinelearning-python, prostate-cancer, prostate-cancer-biopsies, prostate-cancer-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 1.99 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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**:
```bash
git clone https://github.com/yourusername/prostate-cancer-prediction.git
cd prostate-cancer-prediction
```
2. **Install Dependencies**:
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the required libraries:
```bash
pip install -r requirements.txt
```
3. **Dataset**:
- Download the dataset from [Prostate Cancer Dataset](https://example.com/prostate-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:
```bash
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:
```bash
git checkout -b feature-new
```
3. Commit your changes:
```bash
git commit -m "Add new feature"
```
4. Push to the branch:
```bash
git push origin feature-new
```
5. Open a Pull Request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
For inquiries or issues, please contact:
- **Name**: Your Name
- **Email**: [email protected]
- **GitHub**: [S M Mahamudul Hasan](https://github.com/hasansust32/Prostate_Cancer_Predictio)