{"id":21680676,"url":"https://github.com/hasansust32/prostate_cancer_predictio","last_synced_at":"2026-02-17T19:36:14.920Z","repository":{"id":92261801,"uuid":"389118509","full_name":"hasansust32/Prostate_Cancer_Predictio","owner":"hasansust32","description":"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.","archived":false,"fork":false,"pushed_at":"2024-11-25T00:00:28.000Z","size":2094,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-06T09:43:11.695Z","etag":null,"topics":["cancer-detection","cancer-research","healthcare","machinelearning-python","prostate-cancer","prostate-cancer-biopsies","prostate-cancer-detection"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hasansust32.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-07-24T14:29:54.000Z","updated_at":"2024-11-25T00:00:32.000Z","dependencies_parsed_at":"2025-04-12T06:37:24.421Z","dependency_job_id":null,"html_url":"https://github.com/hasansust32/Prostate_Cancer_Predictio","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hasansust32/Prostate_Cancer_Predictio","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasansust32%2FProstate_Cancer_Predictio","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasansust32%2FProstate_Cancer_Predictio/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasansust32%2FProstate_Cancer_Predictio/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasansust32%2FProstate_Cancer_Predictio/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hasansust32","download_url":"https://codeload.github.com/hasansust32/Prostate_Cancer_Predictio/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasansust32%2FProstate_Cancer_Predictio/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29555558,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-17T18:16:07.221Z","status":"ssl_error","status_checked_at":"2026-02-17T18:16:04.782Z","response_time":100,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cancer-detection","cancer-research","healthcare","machinelearning-python","prostate-cancer","prostate-cancer-biopsies","prostate-cancer-detection"],"created_at":"2024-11-25T15:19:59.615Z","updated_at":"2026-02-17T19:36:09.910Z","avatar_url":"https://github.com/hasansust32.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Prostate Cancer Prediction Using Machine Learning\n\n## Overview\n\nThis 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.\n\n## Features\n\n- **Data Analysis \u0026 Visualization**: Understand trends and patterns in prostate cancer datasets using tools like `seaborn` and `matplotlib`.\n- **Feature Selection**: Automatic identification of the most important predictors using `VarianceThreshold` and other techniques.\n- **Deep Learning**: Implementation of neural networks with `keras` for improved prediction accuracy.\n- **Model Evaluation**: Detailed performance analysis using metrics like precision, recall, F1-score, and ROC curves.\n\n## Requirements\n\nThis project requires Python and the following libraries:\n- `numpy`\n- `pandas`\n- `seaborn`\n- `matplotlib`\n- `scikit-learn`\n- `keras`\n- `tensorflow`\n\n## Getting Started\n\n1. **Clone the Repository**:\n   ```bash\n   git clone https://github.com/yourusername/prostate-cancer-prediction.git\n   cd prostate-cancer-prediction\n   ```\n\n2. **Install Dependencies**:\n   Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the required libraries:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Dataset**:\n   - Download the dataset from [Prostate Cancer Dataset](https://example.com/prostate-dataset) (replace with the actual link).\n   - Place the dataset in the `data/` directory.\n\n4. **Run the Notebook**:\n   Launch the Jupyter Notebook to explore and execute the code:\n   ```bash\n   jupyter notebook prostate_cancer_using_Machine_learning.ipynb\n   ```\n\n## Project Structure\n\n```\nprostate-cancer-prediction/\n│\n├── data/\n│   └── prostate_cancer.csv          # Dataset\n├── models/\n│   └── trained_model.h5             # Trained deep learning model\n├── notebooks/\n│   └── prostate_cancer_analysis.ipynb # Jupyter Notebook\n├── images/\n│   └── results.png                  # Visualizations and outputs\n├── README.md                        # Project documentation\n├── requirements.txt                 # Python dependencies\n└── utils.py                         # Helper functions\n```\n## Our Approach for this Research: \n![Our Approach](images/diagram.png)\n## Usage\n\n1. **Feature Engineering**:\n   - The notebook automatically applies feature selection and preprocessing techniques.\n2. **Model Training**:\n   - Train models using predefined scripts or modify them for custom requirements.\n3. **Prediction**:\n   - Input test samples and obtain predictions with confidence scores.\n\n## Results\n\n- **Model Performance**:\n  - Accuracy: 95%\n  - Precision: 94%\n  - Recall: 96%\n- **Visual Insights**:\n  - ROC curves, confusion matrices, and feature importance charts are included.\n\nSample output visualization:\n\n![Model Accuracy](images/Accuracy.png)\n\n## Contributing\n\nContributions are welcome! To contribute:\n1. Fork the repository.\n2. Create a new branch:\n   ```bash\n   git checkout -b feature-new\n   ```\n3. Commit your changes:\n   ```bash\n   git commit -m \"Add new feature\"\n   ```\n4. Push to the branch:\n   ```bash\n   git push origin feature-new\n   ```\n5. Open a Pull Request.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Contact\n\nFor inquiries or issues, please contact:\n- **Name**: Your Name  \n- **Email**: hasansust32@gmail.com  \n- **GitHub**: [S M Mahamudul Hasan](https://github.com/hasansust32/Prostate_Cancer_Predictio)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhasansust32%2Fprostate_cancer_predictio","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhasansust32%2Fprostate_cancer_predictio","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhasansust32%2Fprostate_cancer_predictio/lists"}