Summary of Detection and Forecasting Of Parkinson Disease Progression From Speech Signal Features Using Multilayer Perceptron and Lstm, by Majid Ali et al.
Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM
by Majid Ali, Hina Shakir, Asia Samreen, Sohaib Ahmed
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper proposes a machine learning-based approach for predicting Parkinson’s disease progression, particularly in its early stages. The authors trained Long Short-Term Memory (LSTM) networks using speech signals from Parkinson patients to predict the disease progression up to stage 2-3. Additionally, they employed Multilayer Perceptron (MLP) models to detect the existence of the disease. Feature selection techniques, including Relief-F and Sequential Forward Selection, were used to identify diagnostic features that accurately predicted the disease progression. The proposed approach has the potential to improve the accuracy of Parkinson’s disease diagnosis and monitoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to diagnose and predict the progress of Parkinson’s disease using machine learning. The researchers developed a special type of neural network called LSTM, which looked at speech patterns from people with Parkinson’s to see if it could predict what stage they were in (early or advanced). They also created another kind of neural network called MLP to detect whether someone had Parkinson’s disease or not. By selecting the most important features from this data, they found that their approach was quite good at predicting the disease progression and detecting its existence. This is an important step towards creating a more accurate way to diagnose and monitor Parkinson’s disease. |
Keywords
» Artificial intelligence » Feature selection » Lstm » Machine learning » Neural network
