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

Autor:	Gupta, Richa, Bhandari, Mansi, Grover, Anhad, Al-shehari, Taher, Kadrie, Mohammed, Alfakih, Taha, Alsalman, Hussain
Zdroj:	BioData Mining; 12/2/2024, Vol. 17 Issue 1, p1-11, 11p
Abstrakt:	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 Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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