Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome.

ABSTRACT:

Objective

The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

Methods

The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sens

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9419421 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature