A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. (15th September 2019)

• Record Type: Journal Article
• Authors: Bucholc, Magda; Ding, Xuemei; Wang, Haiying; Glass, David H.; Wang, Hui; Prasad, Girijesh; Maguire, Liam P.; Bjourson, Anthony J.; McClean, Paula L.; Todd, Stephen; Finn, David P.; Wong-Lin, KongFatt
• Is Part Of: Expert systems with applications. Volume 130(2019)
• Page Start: 157
• Page End: 171
• Publication Date: 2019-09-15
• Subjects: Dementia -- Alzheimer's disease -- Decision support system -- Machine learning -- Diagnosis support -- Cognitive impairment; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.04.022
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Integrated machine-learning methods can predict AD severity with high accuracy. Model validation procedure appropriate for processing individual participant data. Highly accessible cognitive and functional markers more accurate than biomarkers. Automated decision-support tool predicts individual AD severity on continuous scale. System assesses undiagnosed patient data against an existing dataset of patients. Abstract: Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.