Aging
Navigate
Submit an Article
Research Paper|Volume 12, Issue 7|pp 6206—6224

Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction

Weimin Zheng1, Bin Cui1, Zeyu Sun2, Xiuli Li2, Xu Han1, Yu Yang3, Kuncheng Li4, Lingjing Hu5, Zhiqun Wang1, Alzheimer's Disease Neuroimaging Initiative
  • 1Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
  • 2Deepwise AI lab, Beijing 100080, China
  • 3Beijing Huading Jialiang Technology Co, Beijing 100000, China
  • 4Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
  • 5Yanjing Medical College, Capital Medical University, Beijing 101300, China
* Equal contribution
# In this article, when performing the cross-test, we used the Data which were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Received: November 5, 2019Accepted: February 25, 2020Published: April 5, 2020
https://doi.org/10.18632/aging.103017

Copyright © 2020 Zheng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

In this paper, we applied a novel method for the detection of Alzheimer’s disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Generalized Split Linearized Bregman Iteration (GSplit LBI). It combines logistic regression and structural sparsity regularizations. In the study, 57 AD patients and 47 normal controls (NCs) were enrolled. We first extracted the entire brain gray matter volume values of all subjects and then used GSplit LBI to build a predictive classification model with a 10-fold full cross-validation method. The model accuracy achieved 90.44%. To further verify which voxels in the dataset have greater impact on the prediction results, we ranked the weight parameters and obtained the top 6% of the model parameters. To verify the generalization of model prediction and the stability of feature selection, we performed a cross-test on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a Chinese dataset and achieved good performances on different cohorts. Conclusively, based on the sMRI dataset, our algorithm not only had good performance in a local cohort with high accuracy but also had good generalization of model prediction and stability of feature selection in different cohorts.