Heart Disease Prediction Model

Overview

This project aims to predict whether a person is suffering from heart disease or not by utilizing machine learning techniques. The model has been developed using Python, the Django framework, and incorporates data science methodologies.

Project Structure

The project is organized into the following components:

1. Data Science:

   • Machine Learning: The heart disease prediction model is built using machine learning techniques.
   • Python: The code for data preprocessing, model training, and evaluation is implemented using Python.

2. Web Application:

   • Django: The web application is developed using the Django framework, allowing for easy deployment and user interaction.
   • Frontend: [Describe any frontend technologies or frameworks used, e.g., HTML, CSS, JavaScript, etc.]

Dataset

The dataset employed in this project is drawn from a cardiovascular clinic, offering a comprehensive view of patient records. Attributes such as age, gender, blood pressure, cholesterol levels, and smoking status are pivotal in predicting heart disease risk.

Project Goal

This project primarily aims to construct a predictive model for heart disease risk utilizing machine learning algorithms. The objective is to showcase the potential of machine learning techniques in the early detection and prevention of heart disease.

Data Exploration

The initial steps involve meticulous data collection and preparation. Data may originate from electronic medical records, surveys, or health-related studies. The dataset undergoes thorough cleaning and preprocessing to ensure accuracy and readiness for analysis. This includes addressing missing data, normalizing values, and converting categorical variables. The emphasis on data cleaning enhances the reliability of the predictive model.

Analysis Techniques

Approaching heart disease prediction as a machine learning project involves deploying various algorithms such as logistic regression, decision trees, and random forests. The performance of these algorithms undergoes rigorous evaluation, with the random forest algorithm emerging as the most accurate predictor, boasting a 90% accuracy rate.

Screenshots:

1. Home Page:

• Describe the purpose and content of the home page.
• Highlight any key features or information presented.

2. Login Page:

• Explain the login functionality and its significance.
• Mention any security measures implemented for user authentication.

3. Input Page:

• Detail the input fields and parameters for the heart disease prediction.
• Highlight any user-friendly design features or validations.

4. Output Page:

• Explain how the machine learning model results are presented.
• Provide insights into the interpretation of the output for users.

Additional Information:

• Mention any other notable pages or functionalities in your project.
• Discuss the technologies used, such as Django for the backend and any frontend frameworks or libraries.
• Highlight the significance of incorporating machine learning into the project.

Results

The project results underscore the effectiveness of machine learning techniques in predicting heart disease risk. The random forest algorithm emerges as the standout performer based on the provided dataset.

Conclusion

The early detection and prediction of heart disease using machine learning techniques offer substantial benefits in healthcare. Regular monitoring of risk factors, coupled with predictive models, contributes to preventive measures, mitigating the impact of heart disease.

How to Use

1. Ensure the installation of necessary dependencies (refer to the Dependencies section).
2. Execute the model.py script to train and save the predictive model.
3. Utilize the Django web application (consult the Views.py and Urls.py scripts) to interact with the predictive model and procure heart disease risk predictions.

List of Dependencies

• NumPy
• Pandas
• Scikit-learn
• Django

Acknowledgements

This project expresses gratitude for the use of the Heart Disease UCI dataset and leverages machine learning techniques to contribute to the early prediction of heart disease.