Abstract

Info

Title:: Advances in auxiliary diagnosis of neuropsychiatric diseases based on machine learning

Author(s):: LEI Bingye1; 2; PAN Jiayu2; WU Fengchun2; 3; LU Xiaobing2; 3; NING Yuping2; 3; CHEN Jun4; 5; WU Kai1; 2; 3; 4; 5; 1. Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; 2. Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; 3. the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou 510370, China; 4. Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China; 5. National Engineering Technology Research Center for Healthcare Devices, Guangzhou 510500, China

Keywords:: Keywords: neuropsychiatric disease; neuroimaging; machine learning; auxiliary diagnosis

Abstract:: Abstract: Neuroimaging techniques are widely used to study the correlations between brain structural/functional abnormalities and neuropsychiatric diseases. Different from traditional statistical analysis methods, machine learning model can realize individualized prediction from neuroimaging data and exploit potential biomarkers. The auxiliary diagnosis of neuropsychiatric diseases includes data preprocessing and machine learning algorithms. Data preprocessing is a kind of artificial feature engineering, providing quantitative features for machine learning algorithms; and machine learning algorithms include feature dimensionality reduction, model training and model evaluation. Robust machine learning algorithms can accomplish accurate predictions for different datasets and provide features that contribute significantly to the prediction as potential biomarkers. Herein the recent advances in auxiliary diagnosis of neuropsychiatric diseases based on machine learning are summarized, including data preprocessing, machine learning algorithms and biomarkers found in previous studies. Finally, the research direction in the future is discussed.