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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. The results demonstrate that the proposed methodology discriminates between Early Decompensation and Not Early groups. We found that the algebraic topology-based machine-learning approach allows us to make accurate predictions from small datasets such as predicting early and not early hepatic decompensation. |
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