Heart Disease Prediction using Machine Learning

This project implements a machine learning model to predict the likelihood of heart disease based on user-provided medical data.

Overview

Heart disease is a significant health concern worldwide, and early detection plays a crucial role in improving patient outcomes. This project leverages machine learning techniques to analyze medical data and predict the likelihood of heart disease in individuals. By identifying individuals at higher risk, healthcare professionals can initiate timely interventions and preventive measures, ultimately reducing the burden of heart-related illnesses.

Dataset

The dataset used in this project contains a comprehensive set of attributes related to patients' medical history, clinical measurements, and diagnostic tests. Features include demographic information (e.g., age, gender), physiological measurements (e.g., cholesterol levels, blood pressure), and diagnostic indicators (e.g., ECG results). Each record in the dataset represents a patient, with a binary label indicating the presence or absence of heart disease.

Methodology

1. Data Preprocessing: The dataset undergoes preprocessing steps to handle missing values, normalize numerical features, and encode categorical variables. This ensures that the data is in a suitable format for training machine learning models.

2. Model Selection and Training: Various machine learning algorithms are evaluated and compared to select the most suitable model for heart disease prediction. Common algorithms such as logistic regression, decision trees, and random forests are considered. The selected model is trained on the preprocessed dataset using appropriate training techniques.

3. Model Evaluation: The trained model is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Cross-validation techniques may be employed to assess the model's generalization ability and robustness.

4. Deployment: Once the model is trained and validated, it can be deployed for practical use. In this project, a user-friendly interface is provided to allow individuals to input their medical data and obtain a prediction of their risk of heart disease.

Usage

1. Installation: Clone the repository to your local machine and install the required dependencies using pip install -r requirements.txt.

2. Run the Program: Execute the main script main.py to launch the command-line interface. Follow the prompts to input your medical data and receive the prediction of heart disease likelihood.

Dependencies

• Python (>=3.6)
• NumPy
• Pandas
• scikit-learn

Contributing

Contributions to the project are welcome! Whether it's adding new features, improving existing functionality, or fixing bugs, contributions from the community help enhance the project's quality and usefulness. To contribute, fork the repository, make your changes, and submit a pull request.

License

This project is licensed under the MIT License. You are free to modify and distribute the code for both commercial and non-commercial purposes.