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A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual
Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Maguire LP, Bjourson AJ et al.. Expert systems with applications, 2019-04-10
Topic: Amyloid, tau and blood-test diagnosis
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 then 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.
Key findings
- We demonstrated high predictive power of CFA.
- We found that KRR and SVM were the best performing regression and classification methods respectively.
- Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.
Why it matters
A computerized decision support system using machine learning found that cognitive and functional assessments had high predictive power for Alzheimer's disease severity, with the best support vector machine performance using four cognitive and functional test scores reaching 83.0% multi-class classification accuracy. This is a diagnostic tool and does not test treating, curing or preventing Alzheimer's disease.
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Study details
- Effect size83.0%; 72.1%; 93.8%
- Evidence strengthLow
How this study scored 47/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 45 of the 96 points we could judge it on, which is 47 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Expert systems with applications) |
| Journal's citation record | 3 / 4 | Expert Systems with Applications: journal h-index 352 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 109 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.1 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 4 / 6 | Cited 9.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 33 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | Ollscoil na Gaillimhe – University of Galway: institution h-index 516 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31402810) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 45 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 45 of the 96 points we could judge it on is 47 out of 100. |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.eswa.2019.04.022
- PubMed: 31402810
- Original source →
- Free full text: Expert Systems with Applications →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Bucholc, M., Ding, X., Wang, H., Glass, D. H., Prasad, G., Maguire, L. P., et al. (2019). A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert systems with applications. https://doi.org/10.1016/j.eswa.2019.04.022
Vancouver
Bucholc M, Ding X, Wang H, Glass DH, Prasad G, Maguire LP, et al. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert systems with applications. 2019. doi:10.1016/j.eswa.2019.04.022. PMID: 31402810.
BibTeX
@article{bucholc2019-31402810,
author = {Bucholc, M. and Ding, X. and Wang, H. and Glass, D. H. and Prasad, G. and Maguire, L. P. and Bjourson, A. J. and others},
title = {{A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual}},
journal = {Expert systems with applications},
year = {2019},
doi = {10.1016/j.eswa.2019.04.022},
pmid = {31402810},
url = {https://doi.org/10.1016/j.eswa.2019.04.022}
}
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