The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics

Abstract

This study investigated the diagnostic accuracy of using an artificial neural network (ANN) for the prediction of metabolic syndrome (MetS) based on socioeconomic status and lifestyle factors. The data of 27,415 subjects who went through examinations and answered questionnaires during three stages from 2006 to 2014 at a health institute in Taiwan were collected and analyzed. The repeated measurements over time were set as predictive factors and used to train and test an ANN for MetS prediction. Among the subjects, 18.3%, 24.6%, and 30.1% were diagnosed with MetS during the respective three stages. ANN analysis applied with an over-sampling technique performed with an area under the curve (AUC) of up to 0.93 based on different models. The over-sampling technique helped improve prediction performance in terms of sensitivity and F₂ measures. The results indicated that waist circumference, socioeconomic status (SES), and lifestyle factors can be utilized in a non-invasive screening tool to assist health workers in making primary care decisions when MetS is suspected. By predicting the occurrence of MetS, individuals or healthcare professionals can then develop preventive strategies in time, thus enhancing the effectiveness of health promotion.

Keywords: artificial neural network; lifestyle factors; metabolic syndrome; socioeconomic status.

Figure 1

(a) AUC/receiver operating characteristic (ROC) curve of the balanced one-stage model; (b) AUC/ROC curve of the balanced two-stage model; (c) AUC/ROC curve of the balanced three-stage model.