Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree

Journal of Neuroscience Methods
Mingwu Jin, Weishu Deng

Abstract

There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data. The neighborhood component analysis (NCA) is applied to select most powerful features for prediction. The ensemble decision tree classifier is built to predict which group the subject belongs to. The best features and model parameters are determined by cross validation of the training data. Our results show that 16 out of a total of 429 features were selected by NCA using 240 training subjects, including MMSE score and structural measures in memory-related regions. The boosting tree model with NCA features can achieve prediction accuracy of 56.25% on 160 test subjects. Principal component analysis (PCA) and sequential feature selection (SFS) are used for feature selection, while support vector machine (SVM) is used for classification. The boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods. The results suggest that NCA be a bett...Continue Reading

Citations

Nov 28, 2018·Annals of Clinical and Translational Neurology·Dan HeXiang Luo
Feb 14, 2021·Journal of Biomechanics·David Jiménez-GrandeDeborah Falla
Apr 9, 2021·Scientific Reports·Haoze Chen, Zhijie Zhang
Jul 7, 2021·Bioprocess and Biosystems Engineering·Yuchen ZhangHongbin Liu

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