Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease
نویسندگان
چکیده
Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, neuroanatomical ageing. Some these estimate subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted and actual chronological can be used an indication pathological deviation normal An important use model biomarker prediction Alzheimer's disease (AD) structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied this specific predictive task, some achieved high accuracy at expense descriptiveness model. This work investigates appropriate combination data science techniques linear models provide, same time, good descriptiveness. proposed method based on workflow that include typical methods, outliers detection, feature selection, regression, logistic regression. In particular, novel inductive bias introduced regression model, aimed improving specificity classification task. compared other AD with without age, including Support Vector Machines Deep Neural Networks. study adopts MRI scans 1, 901 subjects acquired three repositories (ADNI, AIBL, IXI). A only apparent has 88% 92%, respectively, male female subjects, repeated cross-validation analysis, thus achieving comparable or superior performance than state art methods. advantage it maintains semantics input space throughout tasks. accurate also highly descriptive generate potentially insights predictions.
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ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2021
ISSN: ['1662-453X', '1662-4548']
DOI: https://doi.org/10.3389/fnins.2021.673120