Certain Investigations on Classification of Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a persistently progressing neurological disease with restricted treatment choices. The advent of extensive global datasets and advanced machine learning models offers new opportunities to evaluate potential prognostic, inspection, and diagnostic indicators. Additionally, emerging categorization and staging systems aim to accurately stratify patients into distinct prognostic categories. This study evaluates several prominent machine learning classifiers for ALS classification. These experiments have exposed that the CatBoost classification algorithm attained the maximum performance, with an accuracy of 0.85 and an AUC of 0.97. Other significant models include XGB, Random Forest, and Extra-Trees classifiers, each showing an accuracy of around 0.75 but with varying AUC values.