增强的差异性进化算法用于结核性多叶流血特征选择的临床特征分析分析
Xinsen Zhou1, Yi Chen1, Wenyong Gui1
1Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
Artificial intelligence in medicine
|May 15, 2024
概括
早期检测结核性多叶膜溢出是非常重要的. 这项研究引入了用于准确选择特征的增强算法,以及识别及时干预和改善患者结果的关键指标的预测模型.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 算法开发 算法开发
背景情况:
- 结核性多流产呈现出严重的健康风险,可能导致严重的疾病,死亡率和慢性肺病和呼吸系统衰竭等长期并发症.
- 及时诊断和治疗对于减轻不良结果和改善患者预后至关重要.
- 有效的特征选择和预测建模对于早期识别和管理这种情况至关重要.
研究的目的:
- 开发一个增强的差异进化算法,以改善全球优化和特征选择.
- 通过将拟议的算法与支向量机器集成,创建结核性多流的预测模型.
- 确定早期预警的关键临床指标,并改进结核性多叶流血的治疗策略.
主要方法:
- 一个增强的差异进化算法,结合殖民地掠食和分散的食策略,在IEEE CEC 2017数据集上开发和测试.
- 使用二进制版本的算法进行特征选择,对具有不同特征大小 (10到10,000) 的公共数据集进行评估.
- 通过将拟议的算法与支持矢量机器相结合,构建了一个预测模型,该模型在140名患者 (10,780例) 的临床数据上得到验证.
主要成果:
- 增强的差异进化算法展示了强大的全球优化能力.
- 拟议的算法在特征选择方面被证明是有效的,其性能优于可比方法.
- 预测模型成功地确定了结核性多流的显著指标,包括多流腺氨酸脱氨酶,温度,白细胞计数和多流颜色.
结论:
- 开发的算法为复杂数据集中的特征选择提供了一种有效的方法.
- 综合预测模型为结核性多流的早期检测和临床分析提供了有价值的工具.
- 识别关键指标有助于及时干预,可能降低与结核性多支流相关的死亡率和发病率.
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