Application of Machine Learning Models in Predicting Non-Alcoholic Fatty Liver Disease Among Inactive Chronic Hepatitis B Patients: A Cross-Sectional Analysis.

Background/

Objectives

Non-alcoholic fatty liver disease (NAFLD) represents significant health challenges, especially among patients with chronic hepatitis B (CHB). This study uses machine learning models to predict NAFLD in patients with inactive CHB. It builds on previous research by employing classification algorithms to analyze demographic, clinical, and laboratory data to identify NAFLD predictors.

Methods

A single-center cross-sectional study was conducted, including 450 inactive CHB patients from Sultan Qaboos University Hospital. Five ML models were developed: Logistic Regression, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP).

Results

The prevalence of NAFLD was 50.22%. Among the machine learning models, Random Forest achieved the highest performance with an ROC AUC of 0.983 (95% CI: 0.952-0.999), followed by XGBoost at 0.977 (95% CI: 0.938-0.999) and MLP at 0.963 (95% CI: 0.915-0.995). SVM also showed strong performance with an AUC of 0.949 (95% CI: 0.897-0.985), while Logistic Regression demonstrated comparatively lower discrimination with an AUC of 0.886 (95% CI: 0.799-0.952). Key predictive features identified included platelet count, low-density lipoprotein (LDL), hemoglobin, and alanine aminotransferase (ALT). Logistic Regression highlighted platelet count as the most significant negative predictor, while LDL and ALT were positive contributors.

Conclusions

This study shows the utility of ML in improving the identification and management of NAFLD in CHB patients, enabling targeted interventions. Future research should expand on these findings, integrating genetic and lifestyle factors to enhance predictive accuracy across diverse populations.