当前位置： SCI文献检索 > INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS期刊下所有文献 > Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.

Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.

Abstract：

OBJECTIVES:Since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in strategies to improve implementation of the program based on predictive analytics has grown. We investigated the performance of prediction models developed to identify individuals classified as "requiring instruction" (high-risk) who were unlikely to participate in a health intervention program. METHODS:Data were obtained from one large health insurance union in Japan. The study population included individuals who underwent at least one general health check-up between 2008 and 2013 and were identified as "requiring instruction" in 2013. We developed three prediction models based on the gradient boosted trees (GBT), random forest (RF), and logistic regression (LR) algorithms using machine-learning techniques and compared the areas under the curve (AUC) of the developed models with those of two conventional methods The aim of the models was to identify at-risk individuals who were unlikely to participate in the instruction program in 2013 after being classified as requiring instruction at their general health check-up that year. RESULTS:At first we performed the analysis using data without multiple imputation. The AUC values for the GBT, RF, and LR prediction models and conventional methods: 1, and 2 were 0.893 (95%CI: 0.882-0.905), 0.889 (95%CI: 0.877-0.901), 0.885 (95%CI: 0.872-0.897), 0.784 (95%CI: 0.767-0.800), and 0.757 (95%CI: 0.741-0.773), respectively. Subsequently, we performed the analysis using data after multiple imputation. The AUC values for the GBT, RF, and LR prediction models and conventional methods: 1, and 2 were 0.894 (95%CI: 0.882-0.906), 0.889 (95%CI: 0.887-0.901), 0.885 (95%CI: 0.872-0.898), 0.784 (95%CI: 0.767-0.800), and 0.757 (95%CI: 0.741-0.773), respectively. In both analyses, the GBT model showed the highest AUC among that of other models, and statistically significant difference were found in comparison with the LR model, conventional method 1, and conventional method 2. CONCLUSION:The prediction models using machine-learning techniques outperformed existing conventional methods: for predicting participation in the instruction program among participants identified as "requiring instruction" (high-risk).

journal_name

Int J Med Inform

journal_title

International journal of medical informatics

authors

Shimoda A,Ichikawa D,Oyama H

doi

10.1016/j.ijmedinf.2017.12.009

subject

Has Abstract

pub_date

2018-03-01 00:00:00

pages

90-99

eissn

1386-5056

issn

1872-8243

pii

S1386-5056(17)30455-0

journal_volume

111

pub_type

杂志文章

在线工具

Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.