{"id":18736978,"url":"https://github.com/darkdk123/simple-heart-disease-classification","last_synced_at":"2025-11-17T06:30:16.428Z","repository":{"id":241642606,"uuid":"745794057","full_name":"DarkDk123/Simple-Heart-disease-classification","owner":"DarkDk123","description":"This Experiment provides a comprehensive approach to forecast heart disease risks by performing a detailed data analysis, predictive modeling \u0026 hyperparameter tuning. This leads to a `LinearSVC` model with 90% Accuracy","archived":false,"fork":false,"pushed_at":"2024-06-04T04:15:38.000Z","size":4154,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-28T17:14:32.486Z","etag":null,"topics":["classification-algorithm","data-science","data-visualization","exploratory-data-analysis","heart-disease-prediction","machine-learning"],"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/DarkDk123.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":"2024-01-20T06:43:55.000Z","updated_at":"2024-06-09T07:17:22.000Z","dependencies_parsed_at":"2024-06-04T05:27:46.562Z","dependency_job_id":"3354f4aa-be91-48ca-bf66-21292ecc94a8","html_url":"https://github.com/DarkDk123/Simple-Heart-disease-classification","commit_stats":null,"previous_names":["darkdk123/simple-heart-disease-classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DarkDk123%2FSimple-Heart-disease-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DarkDk123%2FSimple-Heart-disease-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DarkDk123%2FSimple-Heart-disease-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DarkDk123%2FSimple-Heart-disease-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DarkDk123","download_url":"https://codeload.github.com/DarkDk123/Simple-Heart-disease-classification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239611993,"owners_count":19668274,"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":["classification-algorithm","data-science","data-visualization","exploratory-data-analysis","heart-disease-prediction","machine-learning"],"created_at":"2024-11-07T15:23:13.789Z","updated_at":"2025-11-17T06:30:16.357Z","avatar_url":"https://github.com/DarkDk123.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **Heart Disease Prediction \u0026 Analysis**\n\nThe aim of this project is to utilize the *Cleveland heart disease* dataset to understand how medical attributes of a person contribute to heart-related diseases. We perform a detailed data analysis and create a machine learning model capable of predicting whether a person has heart disease or not, based on their medical attributes.\n\nThis is a binary classification task. Detailed explanations of the medical attributes are provided in the Jupyter [Notebook](./Heart-disease-model.ipynb).\n\nThis project is a milestone in the [Data Science/ML course](https://github.com/DarkDk123/ZTM-ML-Data-Science) by ZTM.\n\n## Dataset\n\nThe dataset contains medical records of 303 patients collected during their follow-up period, with each patient profile having 13 clinical characteristics and a target variable indicating the presence of heart disease.\n\n- Officially available at [UCI ML Repository](https://archive.ics.uci.edu/dataset/45/heart+disease).\n- Also available in this repository [here](./dataCSV/heart-disease.csv).\n\n### Attributes Information:\n\n| Attribute          | Description                                                                                                                   |\n| ------------------ | ----------------------------------------------------------------------------------------------------------------------------- |\n| **Age**            | Age of a patient [years]                                                                                                      |\n| **Sex**            | Gender of the patient [1: Male, 0: Female]                                                                                    |\n| **ChestPain**      | Chest pain type [0: Typical Angina, 1: Atypical Angina, 2: Non-Anginal Pain, 3: Asymptomatic]                                 |\n| **RestingBP**      | Resting blood pressure in mm Hg (Normal blood pressure - 120/80 Hg)                                                           |\n| **Cholesterol**    | Serum cholesterol level in blood in mg/dL (Normal cholesterol level below for adults 200mg/dL)                                |\n| **FastingBS**      | Fasting Blood Sugar (1 if \u003e 120 mg/dL, 0 otherwise)                                                                           |\n| **RestingECG**     | Resting electrocardiogram results [0: Normal, 1: ST-T wave abnormality, 2: Probable or definite left ventricular hypertrophy] |\n| **MaxHR**          | Maximum heart rate achieved [Numeric value between 60 and 202]                                                                |\n| **ExerciseAngina** | Exercise-induced angina [1: Yes, 0: No]                                                                                       |\n| **Oldpeak**        | ST depression induced by exercise relative to rest                                                                            |\n| **ST_Slope**       | The slope of the peak exercise ST segment [0: Upsloping, 1: Flat, 2: Downsloping]                                             |\n| **CA**             | Number of major vessels (0-3) colored by fluoroscopy                                                                          |\n| **Thal**           | Thallium stress test result [1, 3: Normal, 6: Fixed defect, 7: Reversible defect]                                             |\n| **HeartDisease**   | Output class [1: heart disease, 0: Normal]                                                                                    |\n\nRead more in the data dictionary in the Jupyter [Notebook](./Heart-disease-model.ipynb).\n\n## Procedural Workflow\n\nHere is an overview of the procedural steps performed throughout this project:\n\n1. **Problem Definition**: Defining the objective of predicting heart disease.\n2. **Data Collection \u0026 Exploratory Data Analysis (EDA)**: Gathering data and performing initial analysis.\n3. **Data Preparation \u0026 Preprocessing**: Cleaning and preparing data for modeling. The data was already preprocessed.\n4. **Model Selection, Training, and Validation**: Choosing appropriate models and validating their performance.\n5. **Hyperparameter Tuning \u0026 Model Filtering**: Fine-tuning model parameters for optimal performance.\n6. **Final Model Evaluation**: Evaluating the final model on the test dataset.\n7. **Prediction and Interpretation**: Making predictions and interpreting the results.\n\n## Performance Results\n\nOur final model, evaluated on an unseen test dataset, yields considerable performance metrics:\n\n![Performance Metrics](./Images/performance.png)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdarkdk123%2Fsimple-heart-disease-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdarkdk123%2Fsimple-heart-disease-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdarkdk123%2Fsimple-heart-disease-classification/lists"}