Schizophrenia (SZ) is a mental disorder that causes lifelong disorders based on delusions, cognitive deficits, and hallucinations. By visual assessment, SZ diagnosis is time-consuming and complicated, because brain states are more effectively revealed by electroencephalogram (EEG) signals, which are effectively used in SZ diagnosis. The application of existing deep learning methods in SZ detection is effective in the classification of 2-dimensional images, and these methods require more computational resources. Therefore, dimensionality reduction is necessary for SZ diagnosis using EEG signals. To reduce the dimensionality of the data, an improved CAO (ICAO) dimensionality reduction method is proposed, which integrates horizontal and vertical crossover approaches with AOA. The optimal feature subset is achieved by satisfying the ICAO conditions, and a fitness function is evaluated based on rough sets for improved accuracy in feature selection. Therefore a Crossover-boosted Archimedes optimization algorithm (AOA) with rough sets for Schizophrenia detection (CAORS-SD) was proposed using multichannel EEG signals from both SZ and normal patients. The signals are decomposed using multivariate empirical mode decomposition into multivariate intrinsic mode functions (MIMFs). Entropy metrics such as spectral entropy, permutation entropy, approximate entropy, sample entropy, and SVD entropy are evaluated on the MIMF domain to detect SZ. The processing time of the kernel support vector machine classifier is minimized with fewer features, reducing the risk Fof overfitting. Accuracy, sensitivity, specificity, precision, and F1-score of the CAORS-SD model should be conducted to diagnose SZ. Therefore, the proposed CAORS-SD method achieves the higher performance of accuracy, sensitivity, specificity, precision, and F1-score values of 96.34, 98.95, 96.86, 98.52, and 96.74% respectively. Also, the CAORS-SD method minimizes the error rate and significantly reduces the execution time.

精神分裂症 (SZ) 是一种精神障碍，会导致基于妄想、认知缺陷和幻觉的终身疾病。通过视觉评估，SZ 诊断既耗时又复杂，因为脑电图 (EEG) 信号可以更有效地揭示大脑状态，而脑电图信号可有效用于 SZ 诊断。现有的深度学习方法在SZ检测中的应用在二维图像的分类中是有效的，但这些方法需要更多的计算资源。因此，使用 EEG 信号进行 SZ 诊断需要降维。为了降低数据的维数，提出了一种改进的CAO（ICAO）降维方法，该方法将水平和垂直交叉方法与AOA相结合。通过满足ICAO条件获得最优特征子集，并基于粗糙集评估适应度函数以提高特征选择的准确性。因此，使用来自精神分裂症患者和正常患者的多通道脑电图信号，提出了一种用于精神分裂症检测的带有粗糙集的交叉增强阿基米德优化算法（AOA）（CAORS-SD）。使用多元经验模态分解将信号分解为多元固有模态函数 (MIMF)。在 MIMF 域上评估谱熵、排列熵、近似熵、样本熵和 SVD 熵等熵度量来检测 SZ。使用更少的特征最大限度地减少核支持向量机分类器的处理时间，从而降低 Fof 过度拟合的风险。应通过 CAORS-SD 模型的准确性、敏感性、特异性、精密度和 F1 评分来诊断 SZ。因此，所提出的 CAORS-SD 方法实现了较高的准确性、灵敏度、特异性、精密度和 F1 分数值，分别为 96.34、98.95、96.86、98.52 和 96.74%。此外，CAORS-SD 方法最大限度地降低了错误率并显着缩短了执行时间。