Towards Effective Classification of aMCI Based on Resting-State Multiscale Brain Features and Machine Learning Approaches. (28th June 2021)

• Record Type: Journal Article
• Authors: Cai, Chunting; Yan, Jiqiang; Zhou, Yu; Zheng, Wuyang; Yang, Chenhui; Zhang, Zhemin; Chen, Bokui; Hong, Dan
• Other Names: Zhang Yuanpeng Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-06-28
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/9975237
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the issue that it is challenging to extract effective features from the limited and high-dimensional data, particularly in resting-state data of amnesic mild cognitive impairment (aMCI). Furthermore, the intelligent imaging diagnosis system for aMCI is conductive to make timely predicting groups that may convert to Alzheimer's disease (AD). To improve the system's detection performance and reduce its data redundancy, we first develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to obtain the structural features of brain functional network (BFN). Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. And finally, multiscale features, including structural features and multiscale local features, are fused by concatenation method to further improve the detection performance of aMCI system. Support vector machine based on radial basis function (RBF-SVM) for small data learning is adopted to evaluate the effectiveness of the proposed features. Besides, we employ leave-one-out cross-validation strategy to avoid the overfitting problem of classifier training process. The experiment results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide 86.57% and 86.36%, respectively, which outperforms the traditional methods and offers new insights for accuracy requirements of the aMCI system.