Zengjing Liu, Zhihao Meng, Di Wei, Yuan Qin, Yu Lv, Luman Xie, Hong Qiu, Bo Xie, Lanxiang Li, Xihua Wei, Die Zhang, Boying Liang, Wen Li, Shanfang Qin, Tengyue Yan, Qiuxia Meng, Huilin Wei, Guiyang Jiang, Lingsong Su, Nili Jiang, Kai Zhang, Jiannan Lv, Yanling Hu
OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). METHODS: Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]...
April 25, 2024: BMC Medical Informatics and Decision Making