Abstract
This work analyzes heat transfer in microwave heating process using hyperthermic approach. Predictive performance of artificial intelligence (AI) models coupled with data of mechanistic model were evaluated. The resulting predictive models provide a surrogate representation of the simulated temperature field and may support temperature assessment in microwave hyperthermia applications. The model is developed in two stages, where in the first stage, electromagnetic heating is modeled via solution of bioheat equations to compute temperature field in the tissue. Then, the results are used for building several artificial intelligence-based models. The predictive stage of the study was carried out using three nonlinear kernel-driven learning approaches, namely Support Vector Machine (SVM), Kernel Ridge Regression (KRR), and Relevance Vector Machine (RVM), with the spatial coordinates serving as predictors of the temperature field. Model-specific hyperparameters were determined through a Cuckoo Search-based optimization procedure. Among the evaluated algorithms, SVM delivered the strongest overall predictive performance, yielding R2 values of 0.9766 for the training data and 0.9768 for the test data. Its mean R2 obtained from five-fold cross-validation was likewise 0.9768, with a very small standard deviation of 0.00063, while it also produced the minimum MSE and MAE values. In contrast, RVM exhibited the smallest worst-case deviation, with a maximum absolute error of 9.3946 K. Although KRR and RVM achieved satisfactory test-set R2 values of 0.9200 and 0.9560, respectively, the collective evaluation of predictive accuracy, error magnitude, and validation stability identified SVM as the most suitable approach for dependable and precise estimation of the spatial temperature distribution. These results offer significant insights into the utilization of kernel-based regression models for predicting spatial temperatures, with implications for diverse domains including cancer therapy.