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一种基于沙猫群优化算法的新型SVM用于阿尔茨海默病诊断成像基因组学

Luyun Wang^1,2,3, Jinhua Sheng^1,2, Qiao Zhang^4,5,6

¹School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.

Cerebral cortex (New York, N.Y. : 1991)

|August 15, 2024

概括

这项研究引入了一种新的框架,用于使用脑成像和遗传数据来诊断阿尔茨海默病 (AD). 先进的SS-SCSO-SVM模型在分类认知障碍阶段方面取得了很高的准确性.

科学领域:

神经科学是一个神经科学.
遗传学是一个遗传学.
机器学习机器学习

背景情况:

脑成像基因组学对于理解阿尔茨海默病 (AD) 病理学和早期诊断至关重要.
当前的诊断方法可以通过将神经成像和遗传数据与先进的计算模型集成来增强.

研究的目的:

通过整合功能磁共振成像 (fMRI) 和遗传数据,开发和验证用于诊断阿尔茨海默病 (AD) 的新型框架.
引入和评估一个新的优化算法,SS-SCSO,以提高支持矢量机 (SVM) 模型在AD诊断中的性能.

主要方法:

开发了一个整合fMRI遗传预处理,特征选择和SVM模型的框架.
一个新的沙猫群优化 (SCSO) 算法,SS-SCSO,被提出来优化SVM参数,结合螺旋搜索和警报机制.
使用CEC2017函数对SS-SCSO算法的有效性进行了比较,与使用CEC2017函数的其他元启发算法 (MA) 相比.

主要成果:

SS-SCSO-SVM框架证明了四种认知状态的优秀分类准确性:阿尔茨海默病 (AD),早期轻度认知障碍,晚期轻度认知障碍和健康对照.
拟议的SS-SCSO算法显示,与其他已建立的MA和机器学习技术相比,对AD诊断的探索能力优越.
该框架使用来自阿尔茨海默氏症神经成像倡议的成像遗传数据集进行了验证.

结论:

综合SS-SCSO-SVM框架为早期诊断阿尔茨海默病提供了强大而准确的方法.
新的SS-SCSO优化算法显著提高了SVM模型在复杂的诊断任务中的性能.
这项研究强调了将先进的计算方法与多式联络数据相结合的潜力,以改善神经系统疾病的诊断.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.基因成像基因成像基因成像优化的优化优化优化.沙猫群算法沙猫群算法支持矢量机器的支持矢量机器.

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