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Clinically Explainable Machine Learning Models for Complication Risk in Pediatric Orbital Cellulitis: A Study on Literature-Guided Synthetic Data

Year: 2025

Type: article

Abstract: This repository contains the fully synthetic dataset used for the study titled “Clinically Explainable Machine Learning Models for Complication Risk in Pediatric Orbital Cellulitis: A Study on Literature-Guided Synthetic Data” The dataset consists of 2,000 artificial patient records generated using literature-guided statistical simulation. No real patient data were used and no identifiable information exists. Variables include demographics, clinical presentation, laboratory markers, imaging findings, and a composite complication outcome. The dataset is intended solely for methodological research, reproducibility, and validation of machine learning workflows.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17736323

Author: SEN, Sadri

Institution: Atatürk University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7107855693

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