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Abstract #3925

Automatic classification of Type 2 diabetes mellitus with and without microangiopathy via feature selection and support vector machine based on resting-state fMRI

Jingge Lian1, Jilei Zhang2, Maolin Li1, and Kangan Li*1

1Department of Radiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China, 2Clinical Science, Philips Healthcare, Shanghai, China

Type 2 diabetes (T2DM) mellitus is associated with microvascular complications which 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 with (T2DM-C) and without (T2DM-NC) microangiopathy using ALFF and ReHo 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 in T2DM-C

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