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

Abstract

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 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.

Keywords

• Amyotrophic Lateral Sclerosis (ALS)
• ALS Functional Rating Scale (ALSFRS-R)
• Predictive modeling
• Machine learning
• Disease progression
• XGBoost