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

Table 5 Top 5 variables in the network and sociodemographic and clinical ML logistic regression models

Variable	Coefficient
Top 5 Network Variables the ML Logistic Regression Models
Eigenvector centrality	0.55
Closeness Centrality	0.51
Degree Centrality	0.47
Betweenness Centrality	0.34
Clustering	0.30
Top 5 Sociodemographic/Clinical Variables for the ML Logistic Regression Models
Would Accept a LDKT	0.87
Would Accept a DDKT	0.82
Health	0.75
Would You like More Transplant Info	0.51
Age	0.49

Table 5 shows the top 5 network variables and sociodemographic/health variables in the machine learning models LDKT; Living donor kidney transplant

ISSN: 1471-2369