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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.17376 |
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| _version_ | 1866909301530951680 |
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| author | Marinello, Elena Tavazzi, Erica Longato, Enrico Bosoni, Pietro Dagliati, Arianna Vazifehdan, Mahin Bellazzi, Riccardo Trescato, Isotta Guazzo, Alessandro Vettoretti, Martina Tavazzi, Eleonora Ahmad, Lara Bergamaschi, Roberto Cavalla, Paola Manera, Umberto Chio, Adriano Di Camillo, Barbara |
| author_facet | Marinello, Elena Tavazzi, Erica Longato, Enrico Bosoni, Pietro Dagliati, Arianna Vazifehdan, Mahin Bellazzi, Riccardo Trescato, Isotta Guazzo, Alessandro Vettoretti, Martina Tavazzi, Eleonora Ahmad, Lara Bergamaschi, Roberto Cavalla, Paola Manera, Umberto Chio, Adriano Di Camillo, Barbara |
| contents | Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression Marinello, Elena Tavazzi, Erica Longato, Enrico Bosoni, Pietro Dagliati, Arianna Vazifehdan, Mahin Bellazzi, Riccardo Trescato, Isotta Guazzo, Alessandro Vettoretti, Martina Tavazzi, Eleonora Ahmad, Lara Bergamaschi, Roberto Cavalla, Paola Manera, Umberto Chio, Adriano Di Camillo, Barbara Machine Learning Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction. |
| title | Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.17376 |