{"id":23050184,"url":"https://github.com/aryan4codes/heart_disease_classification","last_synced_at":"2025-04-03T03:27:21.840Z","repository":{"id":188926264,"uuid":"679704291","full_name":"aryan4codes/Heart_Disease_Classification","owner":"aryan4codes","description":"This project focuses on utilizing machine learning techniques to predict heart disease based on patient attributes. 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This project focuses on utilizing machine learning techniques to predict heart disease based on patient attributes. By exploring a comprehensive dataset and applying advanced algorithms, we aim to contribute to the field of data-driven healthcare.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Problem Statement](#problem-statement)\n- [Data](#data)\n- [Project Goals](#project-goals)\n- [Features](#features)\n- [Methodology](#methodology)\n- [Results](#results)\n- [Future Steps](#future-steps)\n- [Getting Started](#getting-started)\n- [License](#license)\n\n## Introduction\n\nIn a world where technology and healthcare intersect, the potential for impactful advancements is limitless. This project seeks to bridge the gap by leveraging machine learning to predict heart disease based on patient attributes. By harnessing the power of data-driven insights, we aim to contribute to improving diagnostic accuracy and patient care.\n\n## Problem Statement\n\nThe core question we address is: Can we accurately predict whether a patient has heart disease using their medical attributes? This challenge combines data science and healthcare to provide valuable insights for medical professionals and researchers.\n\n## Data\n\nWe used a comprehensive dataset from the UCI Machine Learning Repository, sourced from [Kaggle](https://www.kaggle.com/datasets/thisishusseinali/uci-heart-disease-data). This dataset includes attributes such as age, sex, chest pain type, blood pressure, cholesterol levels, and more. These features serve as the foundation for our predictive model.\n\n## Project Goals\n\nOur primary goal during the proof of concept phase was to achieve at least 95% accuracy in predicting heart disease. This ambitious target highlights the potential of machine learning to revolutionize patient care and diagnostics.\n\n## Features\n\nThe key features used for prediction include:\n- Age\n- Gender\n- Chest pain type\n- Resting blood pressure\n- Serum cholesterol levels\n- Fasting blood sugar\n- Resting electrocardiographic results\n- Maximum heart rate achieved\n- Exercise-induced angina\n- ST depression induced by exercise relative to rest\n- Slope of the peak exercise ST segment\n- Number of major vessels colored by fluoroscopy\n\n## Methodology\n\nOur approach involved data preprocessing, feature engineering, model selection, and hyperparameter tuning. We utilized the Logistic Regression algorithm with specific hyperparameters to train the model. The dataset was split into training and testing sets to evaluate the model's performance.\n\n## Results\n\nDuring the proof of concept phase, we achieved an accuracy of [your achieved accuracy] in predicting heart disease. This result demonstrates the potential of machine learning in providing valuable insights for medical professionals and improving patient outcomes.\n\n## Future Steps\n\nAs we move forward, our focus will be on refining the model's accuracy and exploring other advanced algorithms. Additionally, we aim to collaborate with healthcare experts to ensure the model's applicability in real-world scenarios.\n\n## Getting Started\n\nTo explore this project on your local machine, follow these steps:\n\n1. Clone this repository.\n2. Install the necessary dependencies using `pip install -r requirements.txt`.\n3. Run the notebooks in the `notebooks` directory to see the data preprocessing, model training, and evaluation steps.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n---\n\nThank you for visiting the **Decoding Heart Health** project repository. Your interest and contributions are appreciated as we strive to make a positive impact at the intersection of healthcare and technology.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryan4codes%2Fheart_disease_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryan4codes%2Fheart_disease_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryan4codes%2Fheart_disease_classification/lists"}