Abstract
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.
Highlights
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases
Taking an example of MetS risk prediction, Zou et al.[13] set different risk scores for 4 MetS-related risk variables based on hazard ratio (HR) obtained from multiple logistic regression model, and provided a risk model corresponding to the cumulative risk of these indicators, with the area under the receiver operating characteristic curve (AUC) of 0.690
All examination indicators of the previous 2 years with and without differential numerical feature (DNF) and differential state feature (DSF) are considered in experiments. 10-fold cross-validation experiment is carried out, where the metric of AUC is described in mean ± standard deviation (STD), and the best performance in each metric is marked in bold
Summary
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Taking an example of MetS risk prediction, Zou et al.[13] set different risk scores for 4 MetS-related risk variables based on hazard ratio (HR) obtained from multiple logistic regression model, and provided a risk model corresponding to the cumulative risk of these indicators, with the area under the receiver operating characteristic curve (AUC) of 0.690. Another traditional risk prediction method is based on the cut-off value of a single variable. Since MetS are often accompanied by various c omplications[22,23], it is of significance for potential MetS patients to provide effective risk prediction in advance
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