Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier.

ABSTRACT:

Background

Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM.

Methods

Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex rel

SUBMITTER: Wang X 

PROVIDER: S-EPMC7980612 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature