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Cognitive performance classification of older patients using machine learning and electronic medical records
M. Richter, Ewelina Sobotnicka, Adam Bednorz. Scientific Reports, 2025-02-24
Topic: Risk, prevention and care in population studies
Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improving the accuracy of cognitive impairment classification. This study aims to address challenges in current systems by leveraging readily available electronic medical record (EMR) data to simplify and enhance the classification of cognitive impairment. The analysis includes 283 older adults, categorized into three groups: 144 individuals with mild cognitive impairment (MCI), 38 with dementia, and 101 healthy controls. Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. Key predictors for distinguishing healthy controls from individuals with MCI are identified. These are history of myocardial infarction, vitamin D3 levels, the Instrumental Activities of Daily Living (IADL) scale, age, and sodium levels. The nonlinear Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieve the best performance for MCI classification, with an accuracy of 69%, an AUC of 0.75, and a Matthews Correlation Coefficient (MCC) of 0.43. For distinguishing healthy controls from those with dementia, the most influential factors include the IADL scale, the Activities of Daily Living (ADL) scale, education, vitamin D3 levels, and age. Here, the Random Forest algorithm demonstrates superior performance, achieving 84% accuracy, an AUC of 0.96, and an MCC of 0.71. These two models consistently outperform other ML techniques, such as K-Nearest Neighbors, Multi-Layer Perceptron, linear SVM, Naive Bayes, Quadratic Discriminant Analysis, Linear Discriminant Analysis, AdaBoost, and Gaussian Process Classifiers. The findings suggest that EMR data can be an effective resource for the initial classification of cognitive impairments. Integrating these ML-driven approaches into primary care settings may facilitate the early identification of older patients who could benefit from further cognitive assessments.
Key findings
- Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide.
- Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales.
- The findings suggest that EMR data can be an effective resource for the initial classification of cognitive impairments.
Why it matters
Using electronic medical record data from 283 older adults, a support vector machine classified mild cognitive impairment with 69% accuracy and an AUC of 0.75, with predictors including history of myocardial infarction, vitamin D3 levels, and the IADL scale. This is an initial classification tool and does not treat, cure, or prevent Alzheimer's disease.
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Study details
- Participants144
- Evidence strengthLow
How this study scored 49/100
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 senior author's track record, so that was left out: it earned 47 of the 96 points we could judge it on, which is 49 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific Reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 144 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 | 3 / 7 | Cited 15 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 9.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 15 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 | Lukasiewicz Research Network - Krakow Institute of Technology: institution h-index 36 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39994339) |
| Free to read | 2 / 2 | Free to read |
| Points | 47 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 47 of the 96 points we could judge it on is 49 out of 100. |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-025-90460-y
- PubMed: 39994339
- Original source →
- Free full text: Scientific Reports →
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. Check the citation against the original before you use it.
APA 7th
Richter, M., Sobotnicka, E., & Bednorz, A. (2025). Cognitive performance classification of older patients using machine learning and electronic medical records. Scientific Reports. https://doi.org/10.1038/s41598-025-90460-y
Vancouver
Richter M, Sobotnicka E, Bednorz A. Cognitive performance classification of older patients using machine learning and electronic medical records. Scientific Reports. 2025. doi:10.1038/s41598-025-90460-y. PMID: 39994339.
BibTeX
@article{richter2025-39994339,
author = {Richter, M. and Sobotnicka, Ewelina and Bednorz, Adam},
title = {{Cognitive performance classification of older patients using machine learning and electronic medical records}},
journal = {Scientific Reports},
year = {2025},
doi = {10.1038/s41598-025-90460-y},
pmid = {39994339},
url = {https://doi.org/10.1038/s41598-025-90460-y}
}
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