Ai-based Diagnostic Model for Pediatric Exanthematous Diseases
Year: 2026
Type: article
Abstract: Differential diagnosis of pediatric exanthematous diseases remains challenging due to overlapping clinical manifestations. To develop and validate an interpretable AI-based diagnostic model for classification of pediatric exanthematous diseases. A retrospective dataset of pediatric patients with confirmed diagnoses (COVID-19, measles, scarlet fever, chickenpox, allergic reactions) was used. A multi-class logistic regression model was developed. Data were divided into training and test subsets (n = 250). Performance was evaluated using accuracy, precision, recall, and F1-score. The overall classification accuracy reached 99.6%. Precision and recall were 100% for most classes and 98% for measles. Validation confirmed stable generalization. The interpretable AI-based model demonstrates high reliability and scalability for integration into clinical decision-support systems.
Author: Khasanova, Guzal
Institution: Tashkent State Medical University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7128961842
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