This project focuses on the development of a machine learning model based on neural networks for the detection of heart disease. Heart disease is a critical health condition that affects millions of people worldwide. Early detection and diagnosis are crucial for timely intervention and treatment.
The primary dataset used in this project is heart_statlog_cleveland_hungary_final.csv. The dataset contains various health-related features, including patient demographics, medical history, and diagnostic test results.
The project is organized into the following sections:
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Data Preparation and Preprocessing: In this section, we handle data cleaning, missing value imputation, outlier removal, and the encoding of categorical variables to prepare the dataset for training.
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Neural Network Model: We design and implement a neural network model for heart disease detection. The architecture includes multiple layers, activation functions, and optimization techniques.
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Training and Evaluation: We split the dataset into training and testing sets, train the neural network model, and evaluate its performance using relevant metrics.
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Results: This section discusses the project's findings, including model accuracy, precision, recall, and F1-score.
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Dependencies: We list the necessary libraries and dependencies in this section to help users set up their development environment.
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Usage: Instructions for running and using the project are provided here.
To get started with this project, follow these steps:
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Clone this repository to your local machine using
git clone. -
Navigate to the project directory.
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Install the required dependencies by running
pip install -r requirements.txt. -
Run the project according to the instructions provided in the Usage section.
Include instructions on how to run the project, train the neural network model, and evaluate its performance.
# Example usage commands or scripts
jupyter nbconvert --to notebook Project.ipynb