https://github.com/bhaveshbhakta/multiple-disease-prediction
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https://github.com/bhaveshbhakta/multiple-disease-prediction
diabetes-prediction disease-prediction flask heart-disease machine-learning multiple-disease-prediction parkinsons-disease python webdevelopment
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/bhaveshbhakta/multiple-disease-prediction
- Owner: BhaveshBhakta
- Created: 2024-12-18T10:45:44.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-18T11:42:27.000Z (10 months ago)
- Last Synced: 2025-02-12T05:44:36.283Z (8 months ago)
- Topics: diabetes-prediction, disease-prediction, flask, heart-disease, machine-learning, multiple-disease-prediction, parkinsons-disease, python, webdevelopment
- Language: Jupyter Notebook
- Homepage: https://diabetes-prediction-q603.onrender.com
- Size: 6.89 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Multiple Disease Prediction Using Machine Learning
This repository demonstrates a comprehensive machine learning-driven solution for predicting various diseases, including Parkinson's disease, heart disease, and diabetes. By integrating multiple advanced algorithms with an interactive and dynamic web-based interface, the project provides accessible and accurate tools for early diagnosis and healthcare management.
---
## Project Overview
This repository aims to address critical healthcare challenges through predictive models that assist in early detection and management of diseases. With machine learning at its core and a user-friendly interface, the tools are designed to empower both healthcare professionals and patients.
---
## Key Features
### Machine Learning Models
- **Algorithm Diversity:** Utilizes multiple algorithms, such as Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors Classifier, and Support Vector Machines (SVM).
- **Ensemble Learning:** Incorporates a stacking classifier to combine predictions from individual models, delivering the most reliable and accurate outcomes.
### User Interaction
- **Input Parameters:**
- For Parkinson’s Disease: NHR, HNR, d2, PPE, etc.
- For Heart Disease: Age, cholesterol, Fbs, slope, etc.
- For Diabetes: Glucose Level, Blood Pressure, BMI, Insulin Level, etc.
- **Prediction Output:** Based on the provided input, the system predicts whether the disease is present, aiding in risk assessment and decision-making.
### Web Design and User Experience
- **Lenis for Mouse Trails:** Adds a visually dynamic mouse trail effect to enhance user engagement.
- **GSAP Animations:** Implements smooth animations for an appealing and interactive user interface.
- **ScrollTrigger for Dynamic Routing:** Ensures seamless transitions between pages and sections, improving navigation and responsiveness.
---
## Technical Highlights
- **Data Analysis:** Models are trained on comprehensive datasets, ensuring robustness and accuracy.
- **Scalability:** The design allows for future integration of additional diseases, models, and features.
- **Responsive Design:** The interface adapts to various devices, ensuring accessibility across platforms.
---
## Purpose and Applications
The primary objective of this repository is to contribute significantly to healthcare by providing efficient and interactive tools for disease prediction. While these tools are tailored for healthcare professionals and patients, they also serve as excellent educational resources for understanding machine learning applications in medicine.
- **Parkinson’s Disease Prediction:** Assists in identifying early signs of Parkinson’s disease through advanced data-driven insights.
- **Heart Disease Prediction:** Aims to reduce mortality rates by facilitating early detection of heart conditions.
- **Diabetes Prediction:** Supports early diagnosis and effective management of diabetes through precise predictions.
---
## Installation
Follow these steps to set up and run the project on your local system:
1. Clone the repository to your local machine:
```bash
git clone https://github.com/BhaveshBhakta/Multiple-Disease-Prediction.git
```
2. Navigate to the project directory:
```bash
cd Multiple-Disease-Prediction
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the application:
```bash
python app.py
```
5. Open your browser and go to `http://127.0.0.1:5000` to interact with the web application.
## Contributing
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.
## Website Overview
