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https://github.com/prem07a/heart-disease
"Coding a Streamlit web app for heart disease prediction using a trained machine learning model."
https://github.com/prem07a/heart-disease
machine-learning python3 streamlit-webapp
Last synced: 23 days ago
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"Coding a Streamlit web app for heart disease prediction using a trained machine learning model."
- Host: GitHub
- URL: https://github.com/prem07a/heart-disease
- Owner: Prem07a
- License: mit
- Created: 2023-08-01T03:14:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-06T11:53:46.000Z (8 months ago)
- Last Synced: 2024-05-07T07:34:38.007Z (8 months ago)
- Topics: machine-learning, python3, streamlit-webapp
- Language: Jupyter Notebook
- Homepage: https://heart-disease-checking.streamlit.app/
- Size: 1.25 MB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
# Heart Disease Prediction Streamlit App
This is a simple Streamlit web application that allows users to predict the likelihood of heart disease based on input features. The prediction is made using a machine learning model that has been trained on heart disease data.
[Live Website](https://heart-disease-checking.streamlit.app/)
## Table of Contents
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Input Features](#input-features)
- [Output](#output)
## Getting Started
### Prerequisites
To run this application, you need:
- Python (3.6 or later)
- Streamlit
- pandas
- pickle
### Installation
1. Clone this repository:
```bash
git clone https://github.com/Prem07a/Heart-Disease.git
```
2. Navigate to the project directory:
```bash
cd Heart-Disease
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
Run the Streamlit app by executing the following command in your terminal:
```bash
streamlit run ./code/website/app.py
```
The web application will open in your default web browser. You can then interact with the app by inputting various features and clicking the "Predict" button to get the predicted outcome.
## Input Features
The following input features can be adjusted in the app:
- Age
- Sex (Male/Female)
- Chest Pain Type
- Resting Blood Pressure
- Cholesterol
- Fasting Blood Sugar (> 120 mg/dl)
- Resting Electrocardiographic Results
- Maximum Heart Rate Achieved
- Exercise Induced Angina
- ST Depression Induced by Exercise Relative to Rest
- Slope of the Peak Exercise ST Segment
- Number of Major Vessels Colored by Fluoroscopy
- Thalassemia
## Output
After adjusting the input features and clicking "Predict," the app will display the predicted outcome, indicating whether the prediction suggests a positive or negative likelihood of heart disease.
## Thanks
I would like to thank Akshat for reporting a bug in streamlit app.
---