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

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-score.

Results: The 110 surveyed participants' mean age was 60 ± 13 years old. Half (55%) identified as male, and 74% identified as Black. At facility 1, 69% of participants had a positive attitude towards transplantation whereas at facility 2, 45% of participants had a positive attitude. The machine learning logistic regression model using network data alone obtained a higher accuracy and F1 score than the sociodemographic and clinical data model (accuracy 65% ± 5% vs. 61% ± 7%, F1 score 76% ± 2% vs. 70% ± 7%). A model with a combination of both sociodemographic and network data had a higher accuracy of 74% ± 3%, and an F1-score of 81% ± 2%.

Conclusion: Social network data improved the machine learning algorithm's ability to classify attitudes towards kidney transplantation, further emphasizing the importance of hemodialysis clinic social networks on attitudes towards transplant.