Abstract
BACKGROUND: Childhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and leading to a favorable prognosis. OBJECTIVE: This study aimed to identify predictive factors for NAFLD in children and adolescents using machine learning models, focusing on liver biopsy outcomes such as fibrosis, infiltration, ballooning, and steatosis. METHODS: Data from 659 children suspected of NAFLD, who underwent liver biopsy at Mofid Children's Hospital between 2011 and 2023, were analyzed. The dataset included categorical and numerical variables, which were processed using one-hot encoding and standardization. Several machine learning models were trained and evaluated, including CatBoost, AdaBoost, Random Forest, and others. Model performance was assessed using cross-validation with accuracy, precision, recall, F1 score, and ROC-AUC metrics. Feature importance was determined through permutation analysis. RESULTS: Among NAFLD patients, the CatBoost Classifier achieved the highest accuracy (91.8%) and ROC-AUC score (92.3%) in cross-validation. In addition, the adjusted models showed better results. That is, the F1 for the CatBoost raised from 83% to 89% (AUC: 0.86-0.92), for the GradientBoosting from 76% to 81% (AUC: 0.81-0.85), and for Bernolli Naive Bayes from 78% to 82% (AUC: 0.82-0.85). CONCLUSION: Machine learning models, particularly CatBoost, demonstrated strong predictive capabilities for NAFLD diagnosis in children.