Abstract
The diagnostic accuracy for coronary heart disease (CHD) needs to be improved. Some studies have indicated that klotho protein levels upon admission comprise an independent risk factor for CHD and have clinical value for predicting CHD. This study aimed to construct a tool to predict CHD risk by analyzing klotho levels and clinically relevant indicators by using a machine learning (ML) method. We randomly assigned the dataset of the National Health and Nutrition Examination Survey (NHANES) 2007-2016 to training and test sets at a ratio of 70:30. We evaluated the ability of five models constructed using logistic regression, neural networks, random forest, support vector machine, and eXtreme Gradient Boosting to predict CHD. We determined their predictive performance using the following parameters: area under the receiver operating characteristic curve, accuracy, precision, recall, F1, and Brier scores. We analyzed data from 11,583 persons in US NHANES and entered 13 potential predictive variables, including klotho and other clinically relevant indicators, into the feature screening process. We established that the five ML models could predict the onset of CHD. The RF model showed the best predictive performance among the five ML models.