智能医疗服务的前沿:使用新型机器学习方法和元启发算法预测心血管疾病
Fande Kong1,2, Zhengyi Song2,3, Qijia Liu1,2
1Nanjing Institute of Inclusive Child Potential Development, Nanjing, Jiangsu Province, China.
概括
这项研究通过优化差异分析模型增强了心血管疾病 (CVD) 风险预测. 结合线性差异分析与高尔夫球优化算法的LDGO模型,显示了早期干预的卓越准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 心血管疾病 (CVD) 是导致死亡的主要原因,也是全球人口老龄化的一个重要因素.
- 准确的心血管疾病风险评估对于及时有效的早期干预策略至关重要.
- 传统的风险评估模型可以通过先进的计算技术来增强.
研究的目的:
- 研究将优化算法与差别分析模型集成在一起,以改善心血管疾病风险预测的有效性.
- 为了比较不同优化模型的性能,特别关注LDGO模型.
主要方法:
- 使用线性差异分析 (LDA) 和二次差异分析 (QDA) 作为基线模型.
- 集成优化算法,包括高尔夫优化算法 (GOA) 和领袖哈里斯霍克的优化 (LHHO),与LDA和QDA.
- 经验评估了综合模型的性能,特别是LDGO模型 (LDA + GOA).
主要成果:
- LDGO模型的预测准确度很高,在训练阶段达到0.948,在测试阶段达到0.946.
- 与优化算法的集成显著提高了CVD风险评估歧视分析模型的性能.
- 在研究的模型中,LDGO模型是最有效的.
结论:
- 优化的差别分析模型,特别是LDGO,为提高心血管疾病风险预测提供了有希望的方法.
- 这些先进的模型可以促进更准确的早期检测和干预,潜在地减少与心血管疾病相关的死亡率,并改善老龄化人口的健康结果.
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