Parkinson’s Detector System using Python

Parkinson’s Detector System using Python

Abstract:

Parkinson’s Detector System using Python addresses Parkinson’s disease’s diagnosis challenges. Symptom detection is crucial for treating over 6 million people worldwide with this disease. This project improves medical diagnosis using machine learning algorithms and a low-error predictive analytics framework.

Introduction:

PD symptoms worsen over time. One hand tremors and body stiffness are typical. Early PD diagnosis is difficult for non-specialist clinicians. This project uses machine learning to create a Parkinson Detection System.

Objectives:

The Parkinson’s Detector System aims to accurately diagnose Parkinson’s disease. The system uses machine learning algorithms and predictive analytics to improve disease diagnosis, especially in the early stages when symptoms are subtle and often overlooked.

Project Details:

The Parkinson’s Detector System uses front-end and back-end technologies. HTML, CSS, and JavaScript create a user-friendly front-end for data entry. The Python back-end uses Django for data management and database interaction.

The system uses a deep learning Convolutional Neural Network (CNN) model for image recognition. This model is trained on relevant features and labeled Parkinson’s disease instances. The XGBoost algorithm improves system prediction.

To ensure accuracy, the model’s training and testing dataset is carefully curated. It shows the variety of Parkinson’s disease symptoms.

Conclusion:

Python’s Parkinson’s Detector System helps non-specialist clinicians diagnose early-stage Parkinson’s disease. The system reduces error and improves diagnosis by using machine learning algorithms and predictive analytics.

This project proves machine learning can improve medical diagnosis. Early detection and customized treatment are possible with the Parkinson’s Detector System. This system could improve Parkinson’s disease patients’ lives worldwide with further development and clinical implementation.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.