Download Project Document/Synopsis
Parkinson’s disease (PD) is a neurodegenerative movement disorder in which the symptoms gradually worsen over time, beginning with a slight tremor in one hand and a sense of body stiffness. Over 6 million people worldwide are impacted by it. There is currently no conclusive diagnosis for this disease by non-specialist clinicians, especially in the early stages of the disease when symptom identification is very challenging.
Due to their versatility and ease of use, machine learning algorithms have significantly improved the field of medical diagnosis. With the help of our Parkinson Detection System and the suggested predictive analytics framework, the issue can be resolved with a low error rate.
Every person will experience Parkinson’s disease differently. Early symptoms are insignificant and go unnoticed. The majority of the time, symptoms start on one side of your body, progress there, and then spread to the other side. In this system, inputs will be given to determine whether or not Parkinson’s disease exists.
The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL. Here, the CNN model will be used and the algorithm implemented is XGBoost.
Images will be used to detect early Parkinson’s disease.
1. Spiral and wave drawings will be used.
2. The user will need to upload images.
3. The detection will be done as either healthy or Parkinson’s.
Advantages
- It is easy to maintain.
- It is user-friendly.
- Detects the user’s Parkinson’s disease status in a matter of seconds.