ArticleBioData mining2025
WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.
Article in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Cardiac arrhythmia detection via PQRST analyzed data using an optimized hierarchical fused fuzzy deep reinforcement learning.BMC medical informatics and decision making · 2026Article
- Machine learning prediction of adverse events during minutes 15-30 of recovery in patients event-free at 15 min after sedated gastrointestinal endoscopy.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric 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.
methodIn 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.
resultsThe 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 ).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.