Abstract
Clinical data often exhibit heterogenous feature distributions, nonstationary physiological dynamics, and class imbalance, which collectively increase the complexity of accurate prediction. Despite the popularity of support vector machines (SVMs) in clinical data classification, the traditional kernels have globally fixed hyperparameters and may fail to reproduce the local properties of data. We propose an adaptive density–variance (ADV) kernel for the SVM classifier for addressing these shortcomings. The ADV kernel is based on sample‐conditioned bandwidths that adapt to the local neighborhood density and the feature‐level variance within each sample’s k‐nearest neighbors, directly encoding clinical heterogeneity into the similarity space. It is constructed as a combination of valid kernel Hadamard products (elementwise), an adaptive RBF component, and an interaction term, which is a polynomial. This design explicitly captures localized similarity structures alongside global feature correlations. Furthermore, the resulting kernel matrices behave as an empirically verified positive semidefinite (PSD) via eigenspectrum analysis. This kernel is fully sklearn‐compatible via the BaseEstimator interface (available:
https://pypi.org/project/adv-kernel/
). The proposed approach is tested according to 6 benchmark clinical datasets, including breast cancer, heart disease, PIMA diabetes, fetal health, Parkinson’s disease, and chest X‐ray pneumonia. Clinically relevant metrics are compared between the performance and conventional SVM kernels and standard machine learning models. Statistical significance is determined through Friedman and Wilcoxon signed‐rank tests, and an ablation study is performed to determine the contribution of each kernel component. To overcome the black‐box nature, SHAP‐based explainability and permutation feature importance are also implemented. Nested cross‐validation does offer the reliability of the generalization estimates and unbiased model selection. Experimental findings reveal that the ADV kernel has very competitive performance, especially in F1‐score, balanced accuracy, and agreement measures, which means that it is appropriate in heterogeneous and clinically sensitive healthcare contexts.