在缺血性中风中使用基于ANFIS的二进制粒子群优化技术预测心脏病发作增长率II
Afnan Al-Ali1, Uvais Qidwai1, Saadat Kamran2
1Computer Science and Engineering Department, Qatar University, Doha, Qatar.
MethodsX
|September 27, 2023
概括
这项研究介绍了一种结合二进制粒子群集优化和自适应神经模糊推理系统的AI方法,以准确预测缺血性中风中风的增长. 这种新的方法减少了维度和培训时间,提高了预测效率.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 缺血性中风导致脑细胞死亡,原因是血液流动受阻.
- 准确预测心脏病发作增长对于管理中风进展至关重要.
- 像ANFIS这样的现有人工智能方法面临着高维度和长时间培训的挑战.
研究的目的:
- 开发一种创新的,自动的方法,在缺血性中风患者的初始CT扫描后预测第二次心脏病发作的增长.
- 通过减少维度和培训时间来解决传统ANFIS的局限性.
主要方法:
- 这是一种新的方法,将二进制粒子群优化 (BPSO) 与自适应神经模糊推理系统 (ANFIS) 架构相结合.
- 使用皮尔森相关系数和P值选择临床相关特征.
- 拟议模型与传统ANFIS,支持向量回归器 (SVR),浅层神经网络和线性回归相比较.
主要成果:
- 开发的模型在预测心脏病发作增长方面表现出很高的准确性.
- 获得了0.091的根平均平方误差,0.0086的平均平方误差,0.064的平均绝对误差和0.074.074的等号距离.
- 与传统ANFIS相比,BPSO-ANFIS模型在缩小维度和培训时间方面显示出更高的效率.
结论:
- BPSO-ANFIS模型为预测缺血性中风进展提供了更准确,更有效的解决方案.
- 这种方法有可能改善临床决策和中风管理中的患者结果.
- 该研究强调了将优化算法与模糊推理系统相结合的有效性,用于复杂的医学预测.
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