Acta Scientific Paediatrics (ASPE)

Research Article Volume 6 Issue 10

Type 1 Diabetes in Adolescents: About 207 Cases Monitored at the Abass Ndao Hospital Center in Dakar (Senegal)

Srilatha Kadali1,2 Shaik Mohammad Naushad2 and Vijaya Lakshmi Bodiga1*

1Department of Clinical Biochemistry and Molecular Biology, Institute of Genetics and Hospital for Genetics Diseases, Osmania University, Begumpet, Hyderabad, India
2Department of Biochemical Genetics, YODA Lifeline Diagnostics Pvt. Ltd, Ameerpet, Hyderabad, India

*Corresponding Author: Vijaya Lakshmi Bodiga, Department of Clinical Biochemistry and Molecular Biology, Institute of Genetics and Hospital for Genetics Diseases, Osmania University, Begumpet, Hyderabad, India

Received: November 13, 2023; Published: November 27, 2023

Abstract

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. We considered 53 clinical symptoms of MPS patients (n = 255) for the differential diagnosis to derive the model. The diagnosis was based on specific enzyme assays. Among mucopolysaccharidoses, MPS I was the most common disease in our study. Different machine learning tools were tested, out of which classification and regression tree (CART) was more promising. The overall prediction showed 79.92% accuracy in determining the subtype of MPS 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.

Keywords: Mucopolysaccharidoses; Clinical Phenotype; Prenatal Diagnosis; Provisional Diagnosis; Machine Learning

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Citation

Citation: Vijaya Lakshmi Bodiga., et al. “Phenotyping and Provisional Diagnosis of Mucopolysaccharidoses Based on Machine Learning".Acta Scientific Paediatrics 6.10 (2023): 33-43.

Copyright

Copyright: © 2023 Vijaya Lakshmi Bodiga., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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