Studies have found that early diagnosis and administration of drugs can delay the progression of the disease. Currently, AD is still an irreversible condition, and there are no effective medications available today. It is pathologically characterized by the deposition of amyloid-β plaques and tau-related neurofibrillary tangles, resulting in loss of neurons. 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.Īlzheimer’s disease (AD) is a progressive neurodegenerative disease leading to dementia, typically manifesting as memory disturbance, attentional and executive deficits, and visuospatial and perceptual impairments. 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. 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.
#AUTOMATE GSPLIT FULL#
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. In the study, 57 AD patients and 47 normal controls (NCs) were enrolled. It combines logistic regression and structural sparsity regularizations. Specifically, the method involved a new classification algorithm of machine learning, named Generalized Split Linearized Bregman Iteration (GSplit LBI). 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.