Improved stacking ensemble learning based on feature selection to accurately predict warfarin dose.

ABSTRACT:

Background

With the rapid development of artificial intelligence, prediction of warfarin dose via machine learning has received more and more attention. Since the dose prediction involve both linear and nonlinear problems, traditional machine learning algorithms are ineffective to solve such problems at one time.

Objective

Based on the characteristics of clinical data of Chinese warfarin patients, an improved stacking ensemble learning can achieve higher prediction accuracy.

Methods

Information of 641 patients from southern China who had reached a steady state on warfarin was collected, including demographic information, medical history, genotype, and co-medication status. The dataset was randomly divided into a training set (90%) and a test set (10%). The predictive

SUBMITTER: Wang M 

PROVIDER: S-EPMC10834785 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature