{"id":27446328,"url":"https://github.com/reputed-artist/liver-disease-prediction-using-ensemble-machine-learning-method","last_synced_at":"2026-04-11T11:03:29.571Z","repository":{"id":287874184,"uuid":"966073874","full_name":"reputed-artist/Liver-Disease-Prediction-using-Ensemble-Machine-Learning-Method","owner":"reputed-artist","description":"Master degree project for liver disease prediction. i have improved the code reaching its accuracy to 98-99% from 70%.","archived":false,"fork":false,"pushed_at":"2025-04-14T11:39:22.000Z","size":362,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T04:15:56.330Z","etag":null,"topics":["jypyternotebook","lgbmclassifier","machine","machine-learning","machine-learning-algorithms","machinelearning","machinelearning-python","machinelearningprojects","model-training-and-evaluation","numpy","pandas","prediction","python","randomforestclassifier","svm-classifier","xgboost-classifier"],"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/reputed-artist.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":"2025-04-14T11:10:10.000Z","updated_at":"2025-04-14T11:50:11.000Z","dependencies_parsed_at":"2025-04-14T12:39:30.383Z","dependency_job_id":null,"html_url":"https://github.com/reputed-artist/Liver-Disease-Prediction-using-Ensemble-Machine-Learning-Method","commit_stats":null,"previous_names":["reputed-artist/liver-disease-prediction-using-ensemble-machine-learning-method"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reputed-artist%2FLiver-Disease-Prediction-using-Ensemble-Machine-Learning-Method","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reputed-artist%2FLiver-Disease-Prediction-using-Ensemble-Machine-Learning-Method/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reputed-artist%2FLiver-Disease-Prediction-using-Ensemble-Machine-Learning-Method/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reputed-artist%2FLiver-Disease-Prediction-using-Ensemble-Machine-Learning-Method/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/reputed-artist","download_url":"https://codeload.github.com/reputed-artist/Liver-Disease-Prediction-using-Ensemble-Machine-Learning-Method/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249003969,"owners_count":21196793,"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":["jypyternotebook","lgbmclassifier","machine","machine-learning","machine-learning-algorithms","machinelearning","machinelearning-python","machinelearningprojects","model-training-and-evaluation","numpy","pandas","prediction","python","randomforestclassifier","svm-classifier","xgboost-classifier"],"created_at":"2025-04-15T04:16:12.698Z","updated_at":"2026-04-11T11:03:24.538Z","avatar_url":"https://github.com/reputed-artist.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Liver Disease Prediction using Ensemble Machine Learning Techniques\n\nThis repository contains the source code for my **M.Tech project**, focused on improving the prediction accuracy of liver disease using **ensemble machine learning algorithms**.\n\n## 📌 Project Overview\n\nLiver disease is a major cause of mortality worldwide. Early diagnosis can significantly improve patient outcomes. This project aims to develop a predictive model that uses patient data to accurately detect liver disease using an **ensemble of machine learning models**.\n\n## 🎯 Objectives\n\n- To analyze and preprocess liver disease datasets.\n- To apply various machine learning models for classification.\n- To implement **ensemble techniques** to improve predictive accuracy.\n- To evaluate model performance using various metrics.\n- Implement code in both scaled and unscaled data to test results.\n\n## 🛠️ Technologies Used\n\n- **Python 3**\n- **Jupyter Notebook / Google Colab**\n- **Pandas, NumPy** – for data handling\n- **Matplotlib, Seaborn** – for visualization\n- **Scikit-learn** – for machine learning models\n- **XGBoost / Random Forest / LGBM (Light Gradient Boosting Machine Classifier)** – for ensemble modeling\n\n## 🧠 Machine Learning Techniques\n\nThe following models were used and compared:\n\n- Logistic Regression\n- Support Vector Machine (SVM)\n- Random Forest\n- K-Nearest Neighbors (KNN)\n\n### 🔁 Ensemble Models Implemented\n\n- **Bagging** – Random Forest\n- **Boosting** – XGBoost, LGBM\n  \n\n## 📊 Results [vary for scaled and unscaled data]\n\n| Model             | Accuracy (%) |\n|------------------|--------------|\n| Logistic Regression | 74.3%     |\n| SVM                 | 79.1%     |\n| RF                  | 92.1%     |\n| LGBM                | 71.1%     |\n| XGBOOST             | **99.9%** |\n\n\u003e **Voting Classifier** with soft voting gave the best performance, showing that ensemble methods significantly improve the prediction accuracy over individual models.\n\n## 📁 Dataset\n\n- Dataset used: [Indian Liver Patient Dataset (ILPD)](https://www.kaggle.com/datasets/uciml/indian-liver-patient-records)\n- Size: 583 records\n- Features include: Age, Gender, Total Bilirubin, Alkphos Alkaline Phosphotase, etc.\n\n## 🚀 How to Run\n\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/reputed-artist/Liver-Disease-Prediction-using-Ensemble-Machine-Learning-Method.git\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freputed-artist%2Fliver-disease-prediction-using-ensemble-machine-learning-method","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Freputed-artist%2Fliver-disease-prediction-using-ensemble-machine-learning-method","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freputed-artist%2Fliver-disease-prediction-using-ensemble-machine-learning-method/lists"}