Kolmogorov–Arnold Networks for predicting carotid intima-media thickness in cardiovascular risk assessment

Carotid Intima-Media Thickness (CIMT) is defined as a non-invasive and well-validated sign of asymptomatic atherosclerosis and an early predictor of cardiovascular disease (CVD). We assembled a carefully curated dataset of 100 adult patients, encompassing 13 clinical, biochemical and demographic variables routinely collected in outpatient practice. After a five-stage pre-processing pipeline median/mode imputation, categorical encoding, Min–Max scaling, inter-quartile-range outlier removal and SMOTE-NC balancing we trained a Kolmogorov–Arnold Network (KAN) to assign each patient to one of four CIMT-defined risk tiers mentioned as "No", "Low", "Medium", "High". Feature-selection tests (Spearman, Pearson, ANOVA and χ²) removed redundant predictors and improved interpretability. The KAN, implemented with ELU-activated hidden layers and a Softmax output was benchmarked against six conventional algorithms like Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Deep Neural Network, Random Forest and Multi-Layer Perceptron. On stratification of five-fold cross-validation the proposed model achieved 93% accuracy, 93% precision, 93% recall, 91% F1-score and a ROC-AUC of 0.97, outperforming all baseline models by 8–19%. These results demonstrate that KAN's capacity in capturing arbitrary connections and handling multi-class tasks demonstrating its potential as a low-cost and promising tool for early cardiovascular risk hierarchy.