Scholarlink

<p>Pulmonary embolism (PE) can range from minor, asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries, potentially leading to cardiac arrest and fatal outcomes. Due to this significant mortality risk, risk stratification is essential following PE diagnosis to guide appropriate therapeutic intervention. This study proposes a machine learning-based methodology for PE risk stratification, utilizing clinical data from a cohort of 139 patients. The predictive framework integrates an enhanced binary Honey Badger Algorithm (BCCHBA) with the K-Nearest Neighbor (KNN) classifier. To comprehensively evaluate the performance of the core optimization algorithm (CCHBA), a series of benchmark function tests were conducted. Furthermore, diagnostic validation tests were performed using real-world PE patient data collected from medical facilities, demonstrating the clinical significance and practical utility of the BCCHBA-KNN system. Analysis revealed the critical importance of specific indicators, including neutrophil percentage (NEUT%), systolic blood pressure (SBP), oxygen saturation (SaO<sub>2</sub>%), white blood cell count (WBC), and syncope. The classification results demonstrated exceptional performance, with the prediction model achieving 100% sensitivity and 99.09% accuracy. This approach holds promise as a novel and accurate method for assessing PE severity.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Pulmonary Embolism Risk Assessment with an Improved Evolutionary Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年10月8日

查看全文
  • Shuai Liu,
  • Yining Liu,
  • Yangjing Lin,
  • Huiling Chen,
  • Yingying Zhang

摘要

Pulmonary embolism (PE) can range from minor, asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries, potentially leading to cardiac arrest and fatal outcomes. Due to this significant mortality risk, risk stratification is essential following PE diagnosis to guide appropriate therapeutic intervention. This study proposes a machine learning-based methodology for PE risk stratification, utilizing clinical data from a cohort of 139 patients. The predictive framework integrates an enhanced binary Honey Badger Algorithm (BCCHBA) with the K-Nearest Neighbor (KNN) classifier. To comprehensively evaluate the performance of the core optimization algorithm (CCHBA), a series of benchmark function tests were conducted. Furthermore, diagnostic validation tests were performed using real-world PE patient data collected from medical facilities, demonstrating the clinical significance and practical utility of the BCCHBA-KNN system. Analysis revealed the critical importance of specific indicators, including neutrophil percentage (NEUT%), systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell count (WBC), and syncope. The classification results demonstrated exceptional performance, with the prediction model achieving 100% sensitivity and 99.09% accuracy. This approach holds promise as a novel and accurate method for assessing PE severity.