Highly explainable predictive models with DoME for the management of DSP-related harmful algal blooms in the shellfish industry

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      <subfield code="a">Highly explainable predictive models with DoME for the management of DSP-related harmful algal blooms in the shellfish industry</subfield>
      <subfield code="c">Andres Molares Ulloa...[et al.]</subfield>
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      <subfield code="a">The occurrence of HAB has a direct impact on shellfish farming, leading to economic losses due to the contamination of shellfish with toxins harmful to human health. Predicting these blooms accurately is therefore crucial for minimizing their negative effects on the industry. The DoME machine learning model is particularly notable for its high interpretability, as the trained model is expressed as a mathematical equation, allowing for transparent analysis and a better understanding of the factors driving the predictions. This characteristic distinguishes DoME from other black-box models, making it a valuable tool for stakeholders seeking not only accurate predictions but also insights into the dynamics behind HAB events. In this study, we evaluated the novel DoME (Development of Mathematical Expressions) algorithm for the prediction of Harmful Algal Blooms (HAB) associated with Diarrhoeic Shellfish Poisoning (DSP), a significant concern for the shellfish industry. Our testing involved analysing the model's performance in various environmental conditions, demonstrating its robustness and adaptability. DoME achieved a F1-score of 97.80%, which corresponds to an improvement of around 8% over previous studies. This superior performance, combined with its explainability, underscores the model's potential as a practical and reliable solution for early warning systems in the shellfish industry, helping to protect both public health and economic stability</subfield>
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      <subfield code="g">08/12/2025 Volume 28 Number  76 - December 2025 , p. 78 - 91</subfield>
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      <subfield code="d"> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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