Application of the smote and backpropagation neural network (BPNN) techniques in the classification of non- alcoholic fatty liver disease (NAFLD)

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a liver disorder with a high global prevalence and a significant mortality risk. However, clinical NAFLD datasets often exhibit severe class imbalance, causing machine learning models to become biased toward the majority class. This study aims to classify the mortality risk of NAFLD patients using the backpropagation neural network (BPNN) algorithm combined with the synthetic minority over-sampling technique (SMOTE). To ensure model validity, the follow-up time variable (futime) was excluded to prevent data leakage. The experiments were conducted by comparing different data split ratios (70:30, 80:20, and 90:10) as well as various hidden layer configurations and learning rates. The experimental results indicate that, without SMOTE, the model was trapped in the illusion of high accuracy (92%) while failing to detect mortality cases effectively (recall < 15%). In contrast, the application of SMOTE significantly improved the recall value, reaching 79.85% under the 80:20 data split scenario. These findings demonstrate that the integration of SMOTE and BPNN is highly effective in minimizing missed diagnoses (false negatives) in imbalanced medical datasets.