Predictive modeling of ALS progression: an XGBoost approach using clinical features

Predictive modeling of ALS progression: an XGBoost approach using clinical features

Abstract 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 XGBo...

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Bibliographic Details
Main Authors: Richa Gupta, Mansi Bhandari, Anhad Grover, Taher Al-shehari, Mohammed Kadrie, Taha Alfakih, Hussain Alsalman
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BioData Mining
Subjects:
Amyotrophic Lateral Sclerosis (ALS)
ALS Functional Rating Scale (ALSFRS-R)
Predictive modeling
Machine learning
Disease progression
XGBoost
Online Access:https://doi.org/10.1186/s13040-024-00399-5
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https://doi.org/10.1186/s13040-024-00399-5

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