Cardicare is a project that uses advanced machine learning and AI algorithms to detect heart disease from non-invasive medical data analysis. The project is aimed at helping medical professionals to diagnose and treat heart disease more accurately and efficiently.
To install Cardicare, clone the project from Github using the following command:
git clone https://github.com/TABREZ-96/CardicCare
Once the project is cloned, install the necessary dependencies using the following command:
Pip install
Cardicare uses several Python libraries for data analysis, visualization, and machine learning. The following are the main libraries used in the project:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import model_selection
from sklearn import metrics
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import xgboost as xgbCardicare implements several machine learning algorithms to detect heart disease. The following are the models used in the project for comparison:
1.SVM: A support vector machine algorithm, used for binary classification tasks.
2.KNN: A k-nearest neighbors algorithm, used for clustering and classification tasks.
3.Decision Tree: A decision tree algorithm, used for classification tasks.
4.XGBoost: An optimized distributed gradient boosting algorithm, used for ensemble modeling.
To use Cardicare, run the 'Heart.py' file using the following command:
python cardicare.py
The file will train a machine learning model on the provided dataset and display the results in a graphical user interface. The user can interact with the interface to analyze the results and view the accuracy of the model.
If you wish to contribute to Cardicare, feel free to create a pull request or contact me . All contributions are welcome, including bug reports, feature requests, and code contributions.
To contribute code, follow these steps:
- Fork the repository on Github.
- Create a new branch with a descriptive name for your feature or bug fix.
- Make changes to the code and commit them with clear messages.
- Push the changes to your forked repository.
- Open a pull request on the original repository, including a clear description of your changes and why they are necessary.
The project maintainer will review your pull request and provide feedback as necessary. We welcome all contributions, and we are committed to creating a friendly and inclusive community for our contributors.
If you have any questions or concerns, feel free to contact the project maintainer at 
If you found this project helpful or you learned something from it and want to show your appreciation, you can buy me a coffee. Your support will help me to continue maintaining and updating this project.
This project is licensed under the MIT License - see the LICENSE.md file for details