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Predicting Alzheimer's Disease with Interpretable Machine Learning
Jia M, Wu Y, Xiang C, Fang Y.. Dementia and geriatric cognitive disorders, 2023-07-21
Topic: Amyloid, tau and blood-test diagnosis
Introduction This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods We included 1,219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. SHapley Additive exPlanation (SHAP) was applied to identify key predictors of optimal models. Results The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic, self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, logistic regression performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein, and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion Interpretable machine learning showed promise in screening high-risk AD individual and could further identify key predictors for targeted prevention.
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APA 7th
Jia, M., Wu, Y., Xiang, C., & Fang, Y. (2023). Predicting Alzheimer's Disease with Interpretable Machine Learning. Dementia and geriatric cognitive disorders. https://doi.org/10.1159/000531819
Vancouver
Jia M, Wu Y, Xiang C, Fang Y. Predicting Alzheimer's Disease with Interpretable Machine Learning. Dementia and geriatric cognitive disorders. 2023. doi:10.1159/000531819. PMID: 37482057.
BibTeX
@article{jia2023-37482057,
author = {Jia, M. and Wu, Y. and Xiang, C. and Fang, Y.},
title = {{Predicting Alzheimer's Disease with Interpretable Machine Learning}},
journal = {Dementia and geriatric cognitive disorders},
year = {2023},
doi = {10.1159/000531819},
pmid = {37482057},
url = {https://doi.org/10.1159/000531819}
}
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Key findings
- Results The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively.
- For models with sociodemographic, self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, logistic regression performed best, with an AUC value of 0.818.
- Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein, and education level were top five significant predictors.
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Study details
- Participants1,219
- Evidence strengthLow
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 29 of the 58 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Dementia and geriatric cognitive disorders) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,219 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37482057) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 29 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 29 of the 58 points we could judge it on is 50 out of 100. |
| Total | 50 / 100 | — |
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