This project is a Heart Disease prediction system that utilizes machine learning algorithms such as Random Forest, Decision Tree, and Logistic Regression. The system is designed to predict the presence or absence of heart disease based on a set of input features.
The system incorporates the following features:
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Random Forest Algorithm: The Random Forest algorithm is an ensemble learning method that combines multiple decision trees to make predictions. It is known for its high accuracy and ability to handle large datasets.
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Decision Tree Algorithm: The Decision Tree algorithm is a supervised learning method that creates a tree-like model of decisions and their possible consequences. It is used for classification tasks and provides an intuitive representation of the decision-making process.
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Logistic Regression Algorithm: Logistic Regression is a statistical model used for binary classification problems. It estimates the probability of an event occurring based on the input variables.
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Graphical User Interface (GUI): The system's user interface is developed using the Tkinter library in Python. It provides an interactive and user-friendly interface for users to input their data and obtain predictions.
To run this Heart Disease prediction system, you will need the following dependencies:
- Python 3.x
- NumPy
- Pandas
- Scikit-learn
- Tkinter
You can install these dependencies using pip by running the following command:
pip install numpy pandas scikit-learn tkinter
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Clone the repository or download the project files to your local machine.
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Ensure that you have installed all the required dependencies mentioned above.
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Open the terminal or command prompt and navigate to the project directory.
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Run the following command to start the GUI:
python gui.py
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The GUI window will open, allowing you to input the necessary details for the prediction. Fill in the relevant information, such as age, sex, blood pressure, cholesterol levels, etc.
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Once you have provided the required information, click on the "Predict" button.
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The system will process the data using the machine learning algorithms and display the predicted result (presence or absence of heart disease) in the GUI.
The heart disease prediction system utilizes a dataset containing various features associated with heart disease. The dataset used for training and testing the machine learning models is not provided with this project. However, you can obtain heart disease datasets from various sources such as the UCI Machine Learning Repository or Kaggle.
Ensure that your dataset is in a suitable format, with features and corresponding labels, for training the machine learning models.
This Heart Disease prediction system combines machine learning algorithms like Random Forest, Decision Tree, and Logistic Regression with a user-friendly GUI developed using Tkinter. By providing the necessary input data, users can obtain predictions regarding the presence or absence of heart disease. This system can be a useful tool in the early detection and prevention of heart disease.