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Abstract #3910

Application of machine learning multivariate pattern analysis for type 2 diabetes: A resting-state fMRI study

Jingge Lian1, Jilei Zhang2, and Kangan Li1
1Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China, 2Philips Healthcare, Shanghai, China

Type 2 diabetes (T2DM) mellitus can increase risk of cognition impairment and dementia. Recently, machine learning, espicailly support vector machine, were introduced to functional MRI studies in individual classification of diseases. In current study, we used support vector machine to perform individual classification of T2DM and healthy controls (HC) using ALFF features based on rs-fMRI data. The selected features were determined to be key features for classification between groups using recursive feature elimination and may be associated with abnormalities of the spontaneous brain activity

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