AI-Driven Estimation of Electric Field Values from Reference Models via KNN algorithm

Abstract

In this study, we examine the challenges of electromagnetic dosimetry, which are related to the accurate determination of the electric field distribution within biological phantoms. Traditional calculation methods lead to high measurement uncertainties. These uncertainties are due to the geometric complexity and the distribution of electromagnetic properties within these phantoms, which constitute significant obstacles. We propose a machine learning framework using k-nearest neighbor (KNN) regression to estimate and validate field values in cylindrical phantoms modeling human tissue structures. The results we obtain for intra-geometric predictions using multiple datasets demonstrate exceptional performance ( R 2 ≈ 0.99) for phantoms with the same elliptical cylindrical geometry, thanks to robust 5-fold cross-validation. This proves a near-perfect reconstruction of the complex components of the field when trained within identical geometric boundaries. However, inter-geometric extrapolation predicting fields from datasets combining different geometries, including ellipses and cylinders, reveals fundamental limitations. Indeed, the coefficient of determination ( R 2 ) drops significantly to approximately ≈ 0.55 due to intrinsic geometric dissimilarities that hinder the model's transferability. Exhaustive validation, using error analysis such as residual histograms and the superposition of predicted and measured values, consistently confirms these geometry-specific field characteristics. Our results highlight critical constraints in the development of universal dosimetry models. In particular, they underscore the need for geometry-adaptive machine learning architectures, which maintain excellent prediction accuracy for a given geometry while improving cross-domain generalization capabilities.