Development of a Machine Learning-Based Classification Model for Traditional Chinese Medicine Syndrome Differentiation in Wilson Disease

Wilson's disease (WD), or hepatolenticular degeneration, is an autosomal recessive disorder caused by mutations in the ATP7B gene, affecting copper metabolism, with a prevalence of 1/10,000 to 1/30,000 globally and about 5.87 per 100,000 in China. This disease is managed using both traditional Chinese medicine (TCM) and Western medicine, where TCM lacks a specific disease categorization for WD but uses syndrome differentiation like "Liver Wind" and "Tremor Syndrome" for treatment. This differentiation is foundational in TCM, emphasizing the need for precise categorization to enhance treatment efficacy. However, the current TCM approach faces challenges due to the subjective nature of its traditional diagnostic methods and the lack of a standardized differentiation model. To address these issues, the paper proposes developing a machine learning-based classification model for TCM syndromes in WD, aiming to standardize and objectify syndrome differentiation, which could improve the consistency and accuracy of diagnoses and treatments in TCM.