WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data

The performance of WHFDL was assessed and compared against a comprehensive set of classical and state-of-the-art feature selection and classification algorithms. Our results highlighted six key metabolites as biomarkers associated with gastric cancer: (1-Methyladenosine, C18-Carnitine, Guanidineacetic acid, Hypoxanthine, Nicotinamide mononucleotide, and Succinate). The WHFDL outperformed all other classifiers, achieving an F1-score, recall and precision of 94%, 93% and 94%, respectively, along with an accuracy of 94% and an Area Under the Curve of 0.9384. Interpretability were analyzed using SHAP, LIME, IG calibration analysis, and adversarial testing, demonstrating the model's transparency. The source code is available on ( https://github.com/arman-daliri/WHFDL ).