Pediatric Crohn's disease diagnosis aid via genomic analysis and machine learning

Abstract

Introduction: Determination of pediatric Crohn's disease (CD) remains a major diagnostic challenge. However, the rapidly emerging field of artificial intelligence has demonstrated promise in developing diagnostic models for intractable diseases. Methods: We propose an artificial neural network model of 8 gene markers identified by 4 classification algorithms based on Gene Expression Omnibus database for diagnostic of pediatric CD. Results: The model achieved over 85% accuracy and area under ROC curve value in both training set and testing set for diagnosing pediatric CD. Additionally, immune infiltration analysis was performed to address why these markers can be integrated to develop a diagnostic model. Conclusion: This study supports further clinical facilitation of precise disease diagnosis by integrating genomics and machine learning algorithms in open-access database.