Phenotyping and Provisional Diagnosis of Mucopolysaccharidoses Based on Machine Learning

In view of significant overlapping clinical features in mucopolysaccharidoses (MPS) subtypes, clinicians face difficulty in differential diagnosis, thus requiring the need for a machine learning-based clinical tool for the provisional diagnosis of MPS subtypes. Out of 520 patients with suspicion of MPS, 296 patients were identified with MPS types. To develop the model, we took 53 clinical symptoms of MPS patients (n=255) into account for differential diagnosis. The diagnosis was based on enzyme testing. Among mucopolysaccharidoses, MPS I was the most prevalent. Different machine learning tools were examined, and classification and regression tree (CART) emerged as the most promising. The total prediction accuracy in determining the subtype of MPS was 79.92%, with a precision of 89.31%. Phenotype-based provisional diagnosis of MPS can be emerging as a useful effective tool for clinicians, thus eliminating the need to perform a whole panel of enzymes. Improved therapeutic efficacy can be attained through early diagnosis and specific intervention. Additionally, this study estimated the prevalence of MPS disorders and established the disease-specific cut-offs of enzyme activity that can distinguish affected from carriers.