Patient status information and the course of their treatment can provide crucial insights for predicting intervention outcomes (long-term clinical results). We present the results of an analysis of a dataset of patients with bifurcation coronary artery lesions and compare various machine learning approaches, including a novel approach using the Kolmogorov-Arnold neural network (KAN), to address the task of classifying bifurcation coronary artery lesions. We conducted a comparative analysis of the trained models to evaluate their effectiveness. The study was based on a multicenter registry for the treatment of patients with bifurcation coronary artery lesions. In total, 1961 patients were included in the analysis. The main result of this paper is the analysis of the application of KAN and its comparison with the multilayer perceptron (MLP). We demonstrated that the KAN model outperforms traditional machine learning algorithms and the MLP, both in terms of the AUC-ROC metric (0.7127 and 0.5909, respectively) and classification accuracy.