A Review of Parkinson Disease Detection Techniques
Abstract
Parkinson’s disease (PD) is a progressive disorder that is caused by degeneration of nerve cells in the part of the brain called the substantia nigra, which controls movement. These nerve cells die or become impaired, losing the ability to produce an important chemical called dopamine. Studies have shown that symptoms of Parkinson’s develop in patients with an 80 percent or greater loss of dopamine-producing cells in the substantia nigra. Normally, dopamine operates in a delicate balance with other neurotransmitters to help coordinate the millions of nerve and muscle cells involved in movement. Without enough dopamine, this balance is disrupted, resulting in tremor (trembling in the hands, arms, legs and jaw); rigidity (stiffness of the limbs); slowness of movement; and impaired balance and coordination – the hallmark symptoms of Parkinson’s. The cause of Parkinson’s essentially remains unknown. However, theories involving oxidative damage, environmental toxins, genetic factors and accelerated aging have been discussed as potential causes for the disease. In 2005, researchers discovered a single mutation in a Parkinson’s disease gene (first identified in 1997), which is believed responsible for five percent of inherited cases.
Keywords:
Parkinson’s disease, Deep learning, Multi-modal data analysisPublished
Issue
Section
License
Copyright (c) 2024 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Sandra Saji, Melbin Mathew, Angel Mariya S, Amrutha Mugesh, Jincy Lukose, MACHINE LEARNING FOR DETECTION AND PREDICTION OF TOMATO LEAF DISEASES , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Joel Lee George, Karthik S Kumar , Riya Merce Thomas, Roshan Roy Varghese, Simy Mary Kurian, Epidemo A Machine Learning Regression-Based , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Juby Mathew, Maria Jojo, Neha Ann Samson, Noell Biju Michael, Ron T Alumkal, PulseSync: IoT-Enabled Monitoring and Predictive Analytics for Healthcare , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Tintu Alphonsa Thomas, Anishamol Abraham, CNN model to classify visually similar Images , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Nikita Niteen , Simy Mary Kurian, Exploring Explainable AI, Security and Beyond : A Comprehensive Review , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- Ansamol Varghese, Anandhu Anoj, Angel Thomas, Deepta K Sunny, Emil Thomas, TrueNews-AI Powered Detection of Manipulated Text and Images , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Denit D Binny, Diya Mathew, Jaice George, Mehak Riyas, Neenu R, A Comprehensive Survey on EMG-Based Real-Time Gesture Recognition for Prosthetic Hand Applications , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Tom Kurian, Ektha P S, Chethana Raj T, Diona Joseph, Annu Mary Abraham, Intelligent Disease Prediction in Hydroponic Systems Using Machine Learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Anumol V S, Elna S Bijo, Neha Maria Joji, Siya Varghese, Teena George, AI-Based Medicinal Plant Identification Using Deep Learning for Mobile Applications , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Anna N Kurian, Aravind R Nair, Athira Pradeep, Ben V Sajeesh, Traffic Violation Detection Using Machine Learning: A Comprehensive Study , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
You may also start an advanced similarity search for this article.
