Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

• Published in: Journal of Personalized Medicine Vol. 12; no. 7; p. 1026
• Main Authors: Chen, Yang-Yuan, Lin, Chun-Yu, Yen, Hsu-Heng, Su, Pei-Yuan, Zeng, Ya-Huei, Huang, Siou-Ping, Liu, I-Ling
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.06.2022; MDPI
• Subjects: Abdomen; Algorithms; Asian people; Biomarkers; Body mass index; Business metrics; Datasets; Fatty liver; Hepatitis; Hospitals; Immunization; Laboratories; Learning algorithms; Liver cirrhosis; Liver diseases; Machine learning; machine learning; fatty liver disease; predicting; Metabolism; Morbidity; Neural networks; Population; Precision medicine; Primary care; Regression analysis; Software; Support vector machines; Triglycerides; Ultrasonic imaging
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm12071026

The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.