Abstract：
In recent years, brain imaging genomics has advanced significantly in revealing underlying pathological mechanisms of Alzheimer\'s disease (AD) and providing early diagnosis. In this paper, we present a framework for diagnosing AD that integrates magnetic resonance imaging (fMRI) genetic preprocessing, feature selection, and a support vector machine (SVM) model. In particular, a novel sand cat swarm optimization (SCSO) algorithm, named SS-SCSO, which integrates the spiral search strategy and alert mechanism from the sparrow search algorithm, is proposed to optimize the SVM parameters. The optimization efficacy of the SS-SCSO algorithm is evaluated using CEC2017 benchmark functions, with results compared with other metaheuristic algorithms (MAs). The proposed SS-SCSO-SVM framework has been effectively employed to classify different stages of cognitive impairment in Alzheimer\'s Disease using imaging genetic datasets from the Alzheimer\'s Disease Neuroimaging Initiative. It has demonstrated excellent classification accuracies for four typical cases, including AD, early mild cognitive impairment, late mild cognitive impairment, and healthy control. Furthermore, experiment results indicate that the SS-SCSO-SVM algorithm has a stronger exploration capability for diagnosing AD compared to other well-established MAs and machine learning techniques.
摘要：
近年来,脑成像基因组学在揭示阿尔茨海默病（AD）的潜在病理机制和提供早期诊断方面取得了显著进展。在本文中,我们提出了一个诊断AD的框架，该框架集成了磁共振成像（fMRI）基因预处理，特征选择,和支持向量机(SVM)模型。特别是,一种新颖的沙猫群优化(SCSO)算法，名为SS-SCSO，它集成了麻雀搜索算法中的螺旋搜索策略和警报机制，提出了优化支持向量机参数的方法。采用CEC2017基准函数对SS-SCSO算法的优化效能进行评估,与其他元启发式算法(MA)的结果进行比较。所提出的SS-SCSO-SVM框架已被有效地用于使用来自阿尔茨海默病神经影像学计划的成像遗传数据集对阿尔茨海默病的认知障碍的不同阶段进行分类。它在四个典型案例中表现出了出色的分类准确性，包括AD,早期轻度认知障碍，晚期轻度认知障碍，健康的控制。此外，实验结果表明，SS-SCSO-SVM算法与其他完善的MA和机器学习技术相比，具有更强的诊断AD的探索能力。
