{"id":26896062,"url":"https://github.com/sofiasawczenko/heart_failure_forecasting","last_synced_at":"2025-04-01T02:59:32.723Z","repository":{"id":184514614,"uuid":"672008284","full_name":"sofiasawczenko/heart_failure_forecasting","owner":"sofiasawczenko","description":" This heart failure prediction project uses a Kaggle dataset, where several data preprocessing techniques were applied, followed by validations using methods like logistic regression, cross-validation, Naive Bayes, Random Forest, KNN and SVM.","archived":false,"fork":false,"pushed_at":"2024-12-18T10:37:33.000Z","size":267,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-18T11:38:01.751Z","etag":null,"topics":[],"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/sofiasawczenko.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}},"created_at":"2023-07-28T17:12:08.000Z","updated_at":"2024-12-18T10:40:43.000Z","dependencies_parsed_at":"2024-12-18T11:34:29.751Z","dependency_job_id":"a35aecaa-c782-47ae-b4b6-e37f8d853703","html_url":"https://github.com/sofiasawczenko/heart_failure_forecasting","commit_stats":null,"previous_names":["sofiasawczenko/heart_failure_forecasting"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sofiasawczenko%2Fheart_failure_forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sofiasawczenko%2Fheart_failure_forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sofiasawczenko%2Fheart_failure_forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sofiasawczenko%2Fheart_failure_forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sofiasawczenko","download_url":"https://codeload.github.com/sofiasawczenko/heart_failure_forecasting/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246574843,"owners_count":20799221,"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":[],"created_at":"2025-04-01T02:59:32.003Z","updated_at":"2025-04-01T02:59:32.694Z","avatar_url":"https://github.com/sofiasawczenko.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Predicting Heart Failure with Supervised Learning Classification\n\nHeart failure is a critical medical condition where the heart cannot pump enough blood to meet the body’s needs, often due to a weakened or damaged heart muscle. This project applies supervised learning to predict heart failure risk based on medical features. The dataset contains 917 samples and 12 features. The goal is to classify whether a patient has heart disease or not based on these features.\n\n## Project Overview\n\nThis project uses supervised learning to predict the likelihood of heart failure in patients. The model is trained on labeled data to classify patients as either \"at risk of heart failure\" or \"not at risk of heart failure\". The following features are used:\n\n- **Age** (in years)\n- **Sex** (0 for male, 1 for female)\n- **Chest Pain Type** (0 for Typical Angina, 1 for Atypical Angina, 2 for Non-Anginal Pain, 3 for Asymptomatic)\n- **Resting Blood Pressure** (in mmHg)\n- **Cholesterol Level** (in mg/dl)\n- **Fasting Blood Sugar** (0 for \u003c 120 mg/dl, 1 for \u003e= 120 mg/dl)\n- **Resting Electrocardiogram** (0 for Normal, 1 for Abnormality in ST-T wave, 2 for Left Ventricular Hypertrophy)\n- **Maximum Heart Rate**\n- **Exercise-Induced Angina** (0 for No, 1 for Yes)\n- **ST Depression induced by Exercise relative to Rest**\n- **Slope of the ST Segment** (0 for Upsloping, 1 for Flat, 2 for Downsloping)\n- **Heart Disease** (0 for No heart disease, 1 for Presence of heart disease)\n\n## Data Source:\nThe dataset used in this project is from [Kaggle: Heart Failure Prediction](https://www.kaggle.com/fedesoriano/heart-failure-prediction/version/1).\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsofiasawczenko%2Fheart_failure_forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsofiasawczenko%2Fheart_failure_forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsofiasawczenko%2Fheart_failure_forecasting/lists"}