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Background <p>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.</p> Method <p>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.</p> Results <p>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 (<a href="https://github.com/arman-daliri/WHFDL">https://github.com/arman-daliri/WHFDL</a>).</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Nora Mahdavi,
  • Arman Daliri,
  • Mahdieh Zabihimayvan,
  • Yalda Yaghooti,
  • Mohammad Mahdi Mir,
  • Parastoo Ghazanfari,
  • Avin Zarrabi,
  • Pedram Khalaj,
  • Reza Sadeghi

摘要

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).