Locally Alignment Driven Multi Kernel SVM for Robust Prediction on Heterogeneous Biomedical Data

We present a Locally-Alignment Multi-Kernel SVM (LA-MKL) that adaptively combines linear, RBF, and self-tuned RBF kernels using per-sample weights derived from local kernel–target alignment, while remaining PSD-safe. We evaluate our method on two challenging CKD-like datasets (d=24 features): A (n=600) with a ring-plus-cluster structure and 8% MCAR missingness, and B (n=700) with 5% label noise, 30% domain shift, 12% missingness, and heavy-tailed perturbations. Using 5-fold cross-validation, LA-MKL consistently matches strong linear baselines under moderate heterogeneity and outperforms standard kernels under severe non-stationarity, e.g., on CKD-like B, it attains an Accuracy of 0.911±0.021, F1 score of 0.777±0.046, and ROC-AUC of 0.894±0.034, whereas conventional RBF suffers from recall collapse. These results indicate that local alignment–driven weighting effectively emphasizes the most discriminative kernel in each neighborhood, thereby improving robustness to missingness, imbalance, and distributional shifts in biomedical prediction.