Multi-objective feature selection for acute hypotension prediction models: a quantum particle swarm optimization-based study
DOI:
https://doi.org/10.15837/ijccc.2026.2.7065Keywords:
Feature selection, Hypotension, Particle swarm optimization, Multi-objective optimizationAbstract
Acute hypotension is a common and severe clinical condition, closely associated with adverse outcomes such as acute kidney injury and myocardial infarction. Existing research primarily relies on vital sign data to develop predictive models; however, relying solely on these data is insufficient to fully capture the progression of the condition. Integrating laboratory biomarkers can provide a more comprehensive assessment of patient status, thereby significantly improving the model’s accuracy and clinical utility. However, the vast number of available laboratory biomarkers introduces data redundancy and increases complexity, necessitating an efficient feature selection method to identify the most relevant indicators. To overcome limitations of existing methods, this study introduces a novel integration of multi-objective optimization with quantum particle swarm optimization (QPSO), significantly improving indicator selection and prediction accuracy compared to traditional methods. This method optimizes the number of indicators, patient status identification accuracy, sensitivity, and specificity, while setting different priorities for various clinical scenarios. The method was applied to the emergency database of infection patients from the Chinese People’s Liberation Army General Hospital to identify key indicators that align with current clinical needs. The selected indicators demonstrated strong alignment with clinical experience and established guidelines, highlighting their clinical relevance. In the full feature scenario, the model exhibited significant improvements in accuracy, F1 score, and specificity, with accuracy ranging from 89.75% to 94.07% and a peak specificity of 98.51%, outperforming other feature selection methods. Even in high-restriction scenarios, where fewer indicators were available, the model maintained strong performance, with accuracy ranging from 88.66% to 93.38%, demonstrating the method’s resilience and reliability. The proposed multi-objective QPSO method performed excellently across different scenarios, providing an effective tool for indicator selection in acute hypotension prediction and complex medical data analysis. This method enhances the model’s accuracy and clinical applicability, supporting more precise predictions and practical implementation in diverse clinical environments.
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