Weng Shuwei, Hu Die, Chen Jin, Yang Yanyi, Peng Daoquan
Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China.
Diagnostics (Basel). 2023 Mar 18;13(6):1168. doi: 10.3390/diagnostics13061168.
Fatty liver disease (FLD) is an important risk factor for liver cancer and cardiovascular disease and can lead to significant social and economic burden. However, there is currently no nationwide epidemiological survey for FLD in China, making early FLD screening crucial for the Chinese population. Unfortunately, liver biopsy and abdominal ultrasound, the preferred methods for FLD diagnosis, are not practical for primary medical institutions. Therefore, the aim of this study was to develop machine learning (ML) models for screening individuals at high risk of FLD, and to provide a new perspective on early FLD diagnosis.
This study included a total of 30,574 individuals between the ages of 18 and 70 who completed abdominal ultrasound and the related clinical examinations. Among them, 3474 individuals were diagnosed with FLD by abdominal ultrasound. We used 11 indicators to build eight classification models to predict FLD. The model prediction ability was evaluated by the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and kappa value. Feature importance analysis was assessed by Shapley value or root mean square error loss after permutations.
Among the eight ML models, the prediction accuracy of the extreme gradient boosting (XGBoost) model was highest at 89.77%. By feature importance analysis, we found that the body mass index, triglyceride, and alanine aminotransferase play important roles in FLD prediction.
XGBoost improves the efficiency and cost of large-scale FLD screening.
脂肪性肝病（FLD）是肝癌和心血管疾病的重要危险因素，会导致巨大的社会和经济负担。然而，目前中国尚无全国范围的FLD流行病学调查，因此对中国人群进行FLD早期筛查至关重要。遗憾的是，肝活检和腹部超声作为FLD诊断的首选方法，对基层医疗机构并不适用。因此，本研究旨在开发机器学习（ML）模型以筛查FLD高危个体，并为FLD早期诊断提供新视角。
本研究共纳入30574名年龄在18至70岁之间、完成腹部超声及相关临床检查的个体。其中，3474名个体经腹部超声诊断为FLD。我们使用11项指标构建8个分类模型来预测FLD。通过曲线下面积、灵敏度、特异度、阳性预测值、阴性预测值和kappa值评估模型预测能力。通过排列后的Shapley值或均方根误差损失评估特征重要性分析。
在8个ML模型中，极端梯度提升（XGBoost）模型的预测准确率最高，为89.77%。通过特征重要性分析，我们发现体重指数、甘油三酯和丙氨酸氨基转移酶在FLD预测中起重要作用。
XGBoost提高了大规模FLD筛查的效率和成本。
