Figure 2.

Clustering model for patient stratification. The available data consist of basic clinical features; age and BMI. Given this specific ALS patient population, the objective is to explore if patients segregate into specific subgroups. After running a clustering algorithm, we obtain clusters and cluster memberships for each patient. Further analysis of shared traits within the same cluster can help identify novel disease phenotypes. (A) Initial data samples without output. (B) Identify cluster and cluster membership. (C) Stratify samples based on shared feature traits.