{"id":15132693,"url":"https://github.com/chandkund/predicting-heart-disease","last_synced_at":"2026-01-18T00:23:45.878Z","repository":{"id":255678327,"uuid":"852997212","full_name":"chandkund/Predicting-Heart-Disease","owner":"chandkund","description":"Welcome to the Heart Disease Prediction project! 🩺 This project focuses on developing a predictive model to assess heart disease risk based on health indicators like age, cholesterol levels, and blood pressure. 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By analyzing features such as age, cholesterol levels, and blood pressure, we strive to create an effective model to help in early diagnosis and prevention of heart disease.\n\n## Dataset Description\nThe dataset contains various health metrics that are known to influence the likelihood of heart disease. Here are the key features:\n\n- Age: Age of the patient.\n- Sex: Gender of the patient (1 = male, 0 = female).\n- Chest Pain Type: Type of chest pain experienced.\n- Resting Blood Pressure: Blood pressure in mm Hg on admission to the hospital.\n- Cholesterol: Serum cholesterol in mg/dl.\n- Fasting Blood Sugar: Whether fasting blood sugar \u003e 120 mg/dl (1 = true, 0 = false).\n- Resting ECG: Results of the resting electrocardiogram.\n- Max Heart Rate: Maximum heart rate achieved.\n- Exercise Induced Angina: Whether exercise-induced chest pain is present (1 = yes, 0 = no).\n- ST Depression: Depression induced by exercise relative to rest.\n- Target: The presence of heart disease (1 = yes, 0 = no).\n# Project Steps\n\n## Data Exploration:\n\nAnalyzed the dataset to understand the distribution of features.\nVisualized relationships between key features and heart disease risk.\n## Data Preprocessing:\n\nHandled missing values and scaled the numerical features.\nEncoded categorical variables and performed feature selection.\n## Model Building:\n\nDeveloped and compared models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and Neural Networks.\nTuned hyperparameters to optimize model performance.\n## Model Evaluation:\n\nEvaluated models using accuracy_score\nSelected the most effective model for heart disease prediction.\n## Results\nThe final model achieved an accuracy of 82%, demonstrating strong predictive power in identifying individuals at risk of heart disease.\n\n## Conclusion\nThis project underscores the potential of machine learning in healthcare, particularly for early diagnosis and prevention of heart disease. The insights gained could contribute to more personalized healthcare strategies.\n\n## How to Run\nClone the repository.\nInstall the required dependencies: `pip install -r requirements.txt`\nRun the Jupyter Notebook to see the analysis and predictions\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandkund%2Fpredicting-heart-disease","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchandkund%2Fpredicting-heart-disease","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandkund%2Fpredicting-heart-disease/lists"}