A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

Frontiers in Public Health

article

Weidong Ji¹  Mingyue Xue²  Yushan Zhang³  Hua Yao⁴  Yushan Wang⁴ 

[1] Department of Medical Information, Zhongshan School of Medicine, Sun Yat-sen University;Hospital of Traditional Chinese Medicine Affiliated to the Fourth Clinical Medical College of Xinjiang Medical University;Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University;Center of Health Management, The First Affiliated Hospital of Xinjiang Medical University

关键词: machine learning;    screening model;    LASSO;    non-alcoholic fatty liver disease (NAFLD);    predictive models;   

DOI  :  10.3389/fpubh.2022.846118

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.

【 授权许可】

CC BY