Summary of Evaluating Echo State Network For Parkinson’s Disease Prediction Using Voice Features, by Seyedeh Zahra Seyedi Hosseininian et al.
Evaluating Echo State Network for Parkinson’s Disease Prediction using Voice Features
by Seyedeh Zahra Seyedi Hosseininian, Ahmadreza Tajari, Mohsen Ghalehnoie, Alireza Alfi
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study aims to develop a machine learning model for diagnosing Parkinson’s disease (PD) with high accuracy while minimizing false negatives. A feature selection strategy using ANOVA identifies the most informative features, which are then used in various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector Classifier, Extreme Gradient Boosting, and Decision Tree. The results show that ESN achieves exceptional performance with superior accuracy and the lowest false negative rate among all methods, making it an exemplary choice for PD diagnosis, especially in scenarios with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to make a better way to diagnose Parkinson’s disease (PD). They want to find a method that is very accurate but also doesn’t miss too many cases. They try different approaches and find one called Echo State Networks (ESN) works really well. It can tell if someone has PD with high accuracy and doesn’t misdiagnose them much. This could help doctors make better decisions and improve patient care. |
