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| Development and validation of machine learning models for nonalcoholic fatty liver disease |
| Hong-Ye Peng a , b , # , Shao-Jie Duan a , b , # , Liang Pan c , # , Mi-Yuan Wang d , Jia-Liang Chen e , Yi-Chong Wang a , Shu-Kun Yao b , ∗ |
a Graduate School of Beijing University of Chinese Medicine, Beijing 100029, China
b Department of Gastroenterology, China-Japan Friendship Hospital, Beijing 100029, China
c Phase 1 Clinical Trial Center, Deyang People’s Hospital, Deyang 618000, China
d School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
e Center of Integrated Traditional Chinese and Western Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100102, China
∗Corresponding author.
E-mail address: shukunyao@126.com (S.-K. Yao).
# Contributed equally. |
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Abstract Background: Nonalcoholic fatty liver disease (NAFLD) had become the most prevalent liver disease worldwide. Early diagnosis could effectively reduce NAFLD-related morbidity and mortality. This study aimed to combine the risk factors to develop and validate a novel model for predicting NAFLD.
Methods: We enrolled 578 participants completing abdominal ultrasound into the training set. The least absolute shrinkage and selection operator (LASSO) regression combined with random forest (RF) was conducted to screen significant predictors for NAFLD risk. Five machine learning models including logistic regression (LR), RF, extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and support vector machine (SVM) were developed. To further improve model performance, we conducted hyperparameter tuning with train function in Python package ‘sklearn’. We included 131 participants completing magnetic resonance imaging into the testing set for external validation.
Results: There were 329 participants with NAFLD and 249 without in the training set, while 96 with NAFLD and 35 without were in the testing set. Visceral adiposity index, abdominal circumference, body mass index, alanine aminotransferase (ALT), ALT/AST (aspartate aminotransferase), age, high-density lipoprotein cholesterol (HDL-C) and elevated triglyceride (TG) were important predictors for NAFLD risk. The area under curve (AUC) of LR, RF, XGBoost, GBM, SVM were 0.915 [95% confidence interval (CI): 0.886–0.937], 0.907 (95% CI: 0.856–0.938), 0.928 (95% CI: 0.873–0.944), 0.924 (95% CI: 0.875–0.939), and 0.900 (95% CI: 0.883–0.913), respectively. XGBoost model presented the best predictive performance, and its AUC was enhanced to 0.938 (95% CI: 0.870–0.950) with further parameter tuning.
Conclusions: This study developed and validated five novel machine learning models for NAFLD predic- tion, among which XGBoost presented the best performance and was considered a reliable reference for early identification of high-risk patients with NAFLD in clinical practice.
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2023, Vol. 22 
