Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study

• Published in: Hepatobiliary & pancreatic diseases international Vol. 20; no. 5; pp. 409 - 415
• Main Authors: Liu, Yuan-Xing, Liu, Xi, Cen, Chao, Li, Xin, Liu, Ji-Min, Ming, Zhao-Yan, Yu, Song-Feng, Tang, Xiao-Feng, Zhou, Lin, Yu, Jun, Huang, Ke-Jie, Zheng, Shu-Sen
• Format: Journal Article
• Language: English
• Published: Singapore Elsevier B.V 01-10-2021; Department of Hepatobiliary and Pancreatic Surgery,the First Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310003,China%College of Information Science and Electrical Engineering,Zhejiang University,Hangzhou 310027,China%Department of Pathology and Molecular Medicine,Faculty of Health Sciences,McMaster University,1200 Main Street West,Hamilton L8S 4K1,Canada%College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China
• Subjects: Body mass index; Cross-Sectional Studies; Humans; Machine Learning; Non-alcoholic Fatty Liver Disease - diagnosis; Non-alcoholic Fatty Liver Disease - epidemiology; Nonalcoholic fatty liver disease; Population screening; Prediction model; Retrospective Studies; Ultrasonography; Body mass index; Population screening; Nonalcoholic fatty liver disease; Machine learning; Prediction model
• ISSN: 1499-3872
• DOI: 10.1016/j.hbpd.2021.08.004

Nonalcoholic fatty liver disease (NAFLD) is a public health challenge and significant cause of morbidity and mortality worldwide. Early identification is crucial for disease intervention. We recently proposed a nomogram-based NAFLD prediction model from a large population cohort. We aimed to explore machine learning tools in predicting NAFLD.
A retrospective cross-sectional study was performed on 15 315 Chinese subjects (10 373 training and 4942 testing sets). Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models. Nine evaluation indicators including area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, positive predictive value, sensitivity, F1 score, Matthews correlation coefficient (MCC), specificity and negative prognostic value were applied to compare the performance among the models. The selected clinical and biochemical factors were ranked according to the importance in prediction ability.
Totally 4018/10 373 (38.74%) and 1860/4942 (37.64%) subjects had ultrasound-proven NAFLD in the training and testing sets, respectively. Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD. Among these models, the XGBoost model revealed the highest AUROC (0.873), AUPRC (0.810), accuracy (0.795), positive predictive value (0.806), F1 score (0.695), MCC (0.557), specificity (0.909), demonstrating the best prediction ability among the built models. Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores.
The XGBoost model has the best overall prediction ability for diagnosing NAFLD. The novel machine learning tools provide considerable beneficial potential in NAFLD screening.