The company we keep. Using hemodialysis social network data to classify patients' kidney transplant attitudes with machine learning algorithms.

ABSTRACT:

Background

Hemodialysis clinic patient social networks may reinforce positive and negative attitudes towards kidney transplantation. We examined whether a patient's position within the hemodialysis clinic social network could improve machine learning classification of the patient's positive or negative attitude towards kidney transplantation when compared to sociodemographic and clinical variables.

Methods

We conducted a cross-sectional social network survey of hemodialysis patients in two geographically and demographically different hemodialysis clinics. We evaluated whether machine learning logistic regression models using sociodemographic or network data best predicted the participant's transplant attitude. Models were evaluated for accuracy, precision, recall, and F1-sco

SUBMITTER: Aljurbua R 

PROVIDER: S-EPMC9798634 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature