Heart Disease Prediction Using Machine Learning R And Shiny

Abstract

1. Introduction

Creation of a Web application using R and Shiny for prediction of Heart Disease using Machine Learning

We are using three machine learning algorithms namely Naive Bayes, SVM , Decision Tree. The algorithm which has the highest accuracy is implemented in Shiny web app which is SVM at the moment. The user Login and Registration modules are in progress and will be updated soon.

2. Objective

Creation of a Web application using R and Shiny for prediction of Heart Disease using Machine Learning

This project demonstrates how R, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sohambakore/Heart-Disease-Prediction-Using-Machine-Learning-R-and-Shiny-.git
cd Heart-Disease-Prediction-Using-Machine-Learning-R-and-Shiny-

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by sohambakore and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.