학술논문
Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.
Document Type
Academic Journal
Author
Skolariki K; Centre for Dementia Prevention, University of Edinburgh, Scotland, UK. kskolariki@hotmail.com.; Terrera GM; Centre for Dementia Prevention, University of Edinburgh, Scotland, UK.; Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK.; Danso S; Centre for Dementia Prevention, University of Edinburgh, Scotland, UK.; Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK.
Source
Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 0121103 Publication Model: Print Cited Medium: Print ISSN: 0065-2598 (Print) Linking ISSN: 00652598 NLM ISO Abbreviation: Adv Exp Med Biol Subsets: MEDLINE
Subject
Language
English
ISSN
0065-2598
Abstract
There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.