Evaluating an Artificial Immune System-Evolved Decision-Tree Ensemble for Chest X-Ray Classification

Timely and accurate classification of lung diseases from chest X-ray images remains an important healthcare challenge. Machine-learning and deep-learning methods can support automated classification, but their evaluation may be affected by class imbalance, feature redundancy, dataset leakage, and computational cost. This paper evaluates an artificial immune system (AIS)-evolved decision-tree ensemble using fold-specific ResNet18 features. All within-dataset experiments use duplicate-family-aware five-fold splits. Within each fold, standardization and adaptive principal component analysis (PCA) are fitted to the training features, and the Synthetic Minority Over-sampling Technique (SMOTE) is applied only to the reduced training data. Each candidate tree is assigned an affinity based on out-of-bag macro-F1. In the primary run, mean within-dataset macro-F1 was 98.07%, 99.48%, and 98.26% for Datasets 1–3, respectively, and 96.66% for the exploratory Dataset 4. Because of extensive cross-dataset image reuse and conflicting labels, Dataset 4 does not provide independent evidence of clinical lung-cancer detection. A five-seed repeated-initialization analysis repeated the complete fold-specific feature and classification pipeline while preserving the same folds. Mean macro-F1 differences between AIS and the prespecified static comparator for each dataset, calculated as AIS minus the comparator, were −0.18, 0.00, −0.35, and −0.13 percentage points for Datasets 1–4, respectively. Using the same sign convention, mean differences between AIS and the fixed random tree ensemble ranged from −0.05 to +0.05 percentage points. Population diagnostics showed that evolution improved individual-tree macro-F1 but reduced pairwise disagreement, without a consistent majority-vote gain. Median latency from an already decoded image to prediction ranged from 38.86 to 61.20 ms on one CPU thread and from 3.43 to 6.36 ms on an RTX 4090. The results do not establish a practically important or consistent predictive advantage from AIS evolution. The study provides a reproducible and duplicate-controlled framework for evaluating AIS-based tree ensembles.