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WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer
Nora Mahdavi1, Arman Daliri2, Mahdieh Zabihimayvan3
1Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran.
Background:
Gastric Cancer remains one of the most prevalent cancers worldwide, with its prognosis heavily reliant on early detection. Traditional GC diagnostic methods are invasive and risky, prompting interest in non-invasive alternatives that could enhance outcomes.
Method:
In this study, we introduce a non-invasive approach, World Hyper-heuristic Fuzzy Deep Learning, for gastric cancer prediction using metabolomics. Metabolomics profiles of plasma samples from 702 individuals were obtained and used for classification. To apply an efficient feature selection, we employed the World Hyper Heuristic, a metaheuristic to extract the most relevant features from the dataset. Subsequently, the extracted data were classified by implementing a Fuzzy Deep Neural Network.
Results:
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 ).
