Predictive modeling of ALS progression: an XGBoost approach using clinical features
Predictive modeling of ALS progression: an XGBoost approach using clinical features
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Publisher
England: BioMed Central Ltd
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Language
English
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Publisher
England: BioMed Central Ltd
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Contents
This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Function...
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Full title
Predictive modeling of ALS progression: an XGBoost approach using clinical features
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TN_cdi_doaj_primary_oai_doaj_org_article_d8c2674708d54ff4a0aed70e2781d6eb
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d8c2674708d54ff4a0aed70e2781d6eb
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ISSN
1756-0381
E-ISSN
1756-0381
DOI
10.1186/s13040-024-00399-5
