Shengzhen (Guangming) Hospital, University of Chinese Academy of Sciences, Shenzhen 230031, China.
Comput Math Methods Med. 2022 Aug 9;2022:8793659. doi: 10.1155/2022/8793659. eCollection 2022.
To establish a risk prediction model of nonalcoholic fatty liver disease (NAFLD) and provide management strategies for preventing this disease.
A total of 200 inpatients and physical examinees were collected from the Department of Gastroenterology and Endocrinology and Physical Examination Center. The data of physical examination, laboratory examination, and abdominal ultrasound examination were collected. All subjects were randomly divided into a training set (70%) and a verification set (30%). A random forest (RF) prediction model is constructed to predict the occurrence risk of NAFLD. The receiver operating characteristic (ROC) curve is used to verify the prediction effect of the prediction models.
The number of NAFLD patients was 44 out of 200 enrolled patients, and the cumulative incidence rate was 22%. The prediction models showed that BMI, TG, HDL-C, LDL-C, ALT, SUA, and MTTP mutations were independent influencing factors of NAFLD, all of which has statistical significance ( < 0.05). The area under curve (AUC) of logistic regression and the RF model was 0.940 (95% CI: 0.8700.987) and 0.945 (95% CI: 0.8990.994), respectively.
This study established a prediction model of NAFLD occurrence risk based on the RF, which has a good prediction value.
建立非酒精性脂肪性肝病（NAFLD）风险预测模型，为预防该病提供管理策略。
收集消化内科和体检中心的 200 例住院患者和体检者的资料，采集体检、实验室检查和腹部超声检查资料。所有对象均随机分为训练集（70%）和验证集（30%）。构建随机森林（RF）预测模型，预测 NAFLD 的发生风险。采用受试者工作特征（ROC）曲线验证预测模型的预测效果。
200 例纳入患者中 NAFLD 患者 44 例，累积发病率为 22%。预测模型显示 BMI、TG、HDL-C、LDL-C、ALT、SUA 和 MTTP 突变是 NAFLD 的独立影响因素，均有统计学意义（ < 0.05）。Logistic 回归和 RF 模型的曲线下面积（AUC）分别为 0.940（95%CI：0.8700.987）和 0.945（95%CI：0.8990.994）。
本研究建立了基于 RF 的 NAFLD 发生风险预测模型，具有较好的预测价值。
