Summary of Advancing Parkinson’s Disease Progression Prediction: Comparing Long Short-term Memory Networks and Kolmogorov-arnold Networks, by Abhinav Roy et al.
Advancing Parkinson’s Disease Progression Prediction: Comparing Long Short-Term Memory Networks and Kolmogorov-Arnold Networks
by Abhinav Roy, Bhavesh Gyanchandani, Aditya Oza, Abhishek Sharma
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 paper presents a novel approach to predicting Parkinson’s Disease progression using regression methods, Long Short-Term Memory (LSTM) networks, and Kolmogorov Arnold Networks (KAN). The proposed method leverages LSTM and KAN to model complex relationships between motor and non-motor symptoms, enabling early and accurate detection of PD progression. By combining spline-parametrized univariate functions with dynamic learning of activation patterns, KAN outperforms traditional linear models in modeling Parkinson’s Disease progression. This work has the potential to improve patient outcomes by providing a more effective diagnostic tool for clinicians. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on helping doctors diagnose Parkinson’s disease better. The condition affects people’s movements and overall health, making it hard to live with. Current tests are often expensive, time-consuming, and need special equipment. This new approach uses special computer models called Long Short-Term Memory (LSTM) networks and Kolmogorov Arnold Networks (KAN) to predict how the disease will progress over time. This could lead to earlier and more accurate diagnoses, which would help patients get better treatment and improve their quality of life. |
Keywords
» Artificial intelligence » Lstm » Regression
