Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data.

ABSTRACT:

Background

Cardiovascular disease, also known as circulation system disease, remains the leading cause of morbidity and mortality worldwide. Traditional methods for diagnosing cardiovascular disease are often expensive and time-consuming. So the purpose of this study is to construct machine learning models for the diagnosis of cardiovascular diseases using easily accessible blood routine and biochemical detection data and explore the unique hematologic features of cardiovascular diseases, including some metabolic indicators.

Methods

After the data preprocessing, 25,794 healthy people and 32,822 circulation system disease patients with the blood routine and biochemical detection data were utilized for our study. We selected logistic regression, random forest, support vector m

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11439295 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature