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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Patients with end-stage kidney disease (ESKD) frequently experience complications such as anemia, malnutrition, and cardiovascular issues. Serological tests, which are invasive and not routinely conducted, play a crucial role in medical assessments. A non-invasive, convenient method for predicting these test results could significantly enhance patient monitoring.This study develops machine learning models to predict key serological test results using non-invasive bioelectrical impedance measurements, a routine clinical procedure for ESKD patients.
The study employed two machine learning models, Support Vector Machine (SVM) and Random Forest (RF), to predict key serological tests from cellular bioelectrical indicators. Data from 688 patients, comprising 3,872 paired biochemical–bioelectrical records, were used for model validation.
Intra-individual correlation analysis revealed that cell membrane capacitance (Cm) and permittivity(ε) were more strongly associated with biochemical indices. PCA demonstrated that Hb and Ca accounted for >99.8% of total variance across all biochemical indices. Both SVM and RF models demonstrated effective classification of key serological results (albumin, phosphorus, parathyroid hormone) into low, normal, and high. The random forest (RF) model outperformed SVM in classifying most biochemical indices, with F1-scores, precision, and recall exceeding 0.6 (mostly >0.7) in males and consistently >0.6 in females. Notably, RF achieved exceptional performance for Hb (females) and Alb (both sexes). Confusion matrix analysis indicated higher accuracy in predicting male PTH and female P, while revealing misclassification tendencies for Alb (both genders) and male Ca due to sample imbalance.
The machine learning models effectively estimated serological test results for maintenance hemodialysis patients based on bioelectrical impedance parameters.
