Summary of Machine Learning For Alsfrs-r Score Prediction: Making Sense Of the Sensor Data, by Ritesh Mehta et al.
Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data
by Ritesh Mehta, Aleksandar Pramov, Shashank Verma
First submitted to arxiv on: 10 Jul 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 The paper explores the use of sensor-derived data from an app to forecast the progression of Amyotrophic Lateral Sclerosis (ALS) using machine learning models. The authors leverage the iDPP@CLEF 2024 challenge dataset and evaluate various predictive models, including naive and ElasticNet regression. The results show that the naive model achieved a Mean Absolute Error (MAE) of 0.20 and a Root Mean Square Error (RMSE) of 0.49, while the ElasticNet model recorded an MAE of 0.22 and an RMSE of 0.50. This study contributes to the development of predictive models for ALS using sensor data, with potential applications in early detection and tailored care strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a phone app to help predict how fast someone’s ALS will get worse. The app collects data from sensors that measure things like movement and balance. The researchers used this data to test different machine learning models to see which one worked best. They found that a simple model did better than a more complex one, but both were able to make good predictions about the progression of ALS. |
Keywords
» Artificial intelligence » Machine learning » Mae » Regression
