Prediction of Parkinson’s Disease: A Comprehensive Review

Prediction of Parkinson’s Disease: A Comprehensive Review

Y V Sai Parimala, Vishal S Naik, Shrusti P Hulekal, Sushma R, Karthik B R, Dr. Prabhanjan S

Dept of CSE

Jyothy Institute Of Technology

Bangalore India

Abstract— Parkinson's disease (PD) has obtained a lot of attention as it impacts people's lives gradually over time. Early Parkinson’s Disease diagnosis and therapy can manage the disease's progression, provide symptom relief, and enhance the patient’s quality of life. However, because the early symptoms of Parkinson’s Disease are rarely visible in day-to-day life, the current method of Parkinson’s Disease diagnosis is carried out in a clinical setting and administered by a Parkinson’s Disease expert. According to the CDC/NIH study, Parkinson’s Disease is typically diagnosed two to ten years after symptoms first appear. Therefore, the need for a more understandable Parkinson’s Disease diagnosis is crucial. Notably, a variety of studies have studied the prospect of adopting wearable and mobile personal devices to identify PD symptoms and have shown encouraging findings. It offers chances to move early PD diagnosis from the clinical setting to regular life. This survey aims to provide a detailed analysis of technologies for Parkinson’s Disease detection from 2000 to 2021. It compares their advantages and disadvantages in real-world settings and offers suggestions for bridging the performance gap between cutting-edge clinical approaches.

Keywords— Parkinson, CNN, machine learning, classification