{"id":27644689,"url":"https://github.com/komailmk/anemia-detection-using-machine-learning","last_synced_at":"2026-05-02T03:08:43.973Z","repository":{"id":289146820,"uuid":"970271762","full_name":"KomailMK/anemia-detection-using-machine-learning","owner":"KomailMK","description":"This Repo Contains Code for Anemia Disease Prediction using Machine Learning. A smart Flask app that uses clinical test results and image data to predict anemia with machine learning, in a user-friendly interface.","archived":false,"fork":false,"pushed_at":"2025-04-23T20:02:55.000Z","size":112,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-24T00:58:25.503Z","etag":null,"topics":["data-science","deep-learning","flask","machine-learning"],"latest_commit_sha":null,"homepage":"https://mycodespaceio.pythonanywhere.com/","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/KomailMK.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":"2025-04-21T18:49:58.000Z","updated_at":"2025-04-23T20:03:00.000Z","dependencies_parsed_at":"2025-04-21T19:49:07.604Z","dependency_job_id":null,"html_url":"https://github.com/KomailMK/anemia-detection-using-machine-learning","commit_stats":null,"previous_names":["komailmk/anemia-predict-using-ml","komailmk/anemia-detection-using-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Fanemia-detection-using-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Fanemia-detection-using-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Fanemia-detection-using-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Fanemia-detection-using-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KomailMK","download_url":"https://codeload.github.com/KomailMK/anemia-detection-using-machine-learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250540999,"owners_count":21447427,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["data-science","deep-learning","flask","machine-learning"],"created_at":"2025-04-24T00:58:29.165Z","updated_at":"2026-05-02T03:08:43.968Z","avatar_url":"https://github.com/KomailMK.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AnemiaX: A Flask Web App for Anemia Prediction using Clinical and Image Data\n\nAnemiaX is a dual-model web application designed to predict anemia disease using two types of inputs:\n- Clinical test data (e.g. Hemoglobin levels, Hematocrit, RBC counts)\n- Image data (based on Red, Green, and Blue pixel percentages)\n\nThis project uses machine learning and deep learning models for prediction, integrated into a seamless, responsive, and visually appealing Flask web application.\n\n##  Features\n\n-  Predict anemia from standard clinical test inputs\n-  Predict anemia based on pixel color analysis of images\n-  Flask backend with a clean HTML, CSS, and JavaScript frontend\n-  Integrated data preprocessing, SMOTE, and hyperparameter tuning\n-  Uses SVM, Random Forest, Logistic Regression, XGBoost, and deep learning models\n-  Deep learning models built using TensorFlow/Keras\n-  Modular structure for easy deployment and scalability\n-  Access it here: https://mycodespaceio.pythonanywhere.com/\n\n---\n\n## ML and DL Models Used\n\n- Logistic Regression, SVC, Decision Tree, Random Forest, XGBoost\n- Deep Neural Networks using Keras with LSTM, GRU, and Conv1D layers\n- SMOTE used for handling class imbalance\n- StandardScaler and MinMaxScaler for feature scaling\n\n---\n\n## Dataset\n\n- **Clinical Test Data**: Includes features like Hemoglobin, Hematocrit, MCV, MCH, etc.\n- **Image Data**: Converted image pixel data to RGB percentages to use as input for prediction\n\n\u003e Note: Models are still under active development as we're still collecting data.\n\n---\n\n## How to Run Locally\n\n1. **Clone the Repository**\n\n```bash\n  git clone https://github.com/yourusername_here/anemia-detection-using-machine-learning.git\n  cd anemia-detection-using-machine-learning\n```\n\n2. **Create a Virtual Environment**\n\n```bash\n  python -m venv venv\n  source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n```\n\n3. **Install Dependencies**\n\n```bash\n  pip install -r requirements.txt\n```\n\n4. Run The App\n```bash\n  python app.py\n```\n\u003e App will be live at: http://127.0.0.1:5000/\n---\n\n## Frontend Design\n\n  - Fully responsive HTML/CSS layout\n  - Vanilla JavaScript for smooth transitions and validation\n  - Clean UI/UX design for better accessibility and flow\n---\n\n## Future Improvements\n\n  - Integrate SHAP for model explainability\n  - Add user authentication system\n  - Deploy to cloud (Render, Heroku, or AWS)\n  - Add PDF report download after prediction\n---\n\n#  License\nThis project is open-source and available under the [MIT License](LICENSE).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkomailmk%2Fanemia-detection-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkomailmk%2Fanemia-detection-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkomailmk%2Fanemia-detection-using-machine-learning/lists"}