Machine-learning-based solutions need sufficient manually labeled training data to produce accurate predictions, which can hinder their performance for rare diseases with limited data. We show how to use a newly developed algebraic topology-based machine learning method that analyzes the visual pattern of the data to accurately predict hepatic decompensation in patients with Primary Sclerosing Cholangitis.