Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population.

ABSTRACT:

Background

Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development.

Methods

A total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked.

Results

Nine SNPs were significantly associated with COP

SUBMITTER: Ma X 

PROVIDER: S-EPMC7110698 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature