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Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods
Gómez-Ramírez J, Ávila-Villanueva M, Fernández-Blázquez MÁ.. Scientific reports, 2020-11-26
Topic: Risk, prevention and care in population studies
Alzheimer's Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.
Key findings
- Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia.
Why it matters
Using machine learning on self-reported information from a large Spanish aging study, subjective cognitive decline stood out as the most important feature for future conversion to mild cognitive impairment. This identifies risk factors for prediction and does not treat, cure or prevent Alzheimer's disease.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 56/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported and randomised / blinded because the record does not state it.
| 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 | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — 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 | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (pubmed indexes it as a clinical or observational study) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 118 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.8 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 9.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 61 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Miguel A. Fernández‐Blázquez: h-index 20 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Instituto de Salud Carlos III: institution h-index 649 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33244011) |
| Free to read | 2 / 2 | Free to read |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-020-77296-4
- PubMed: 33244011
- 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
Gómez-Ramírez, J., Ávila-Villanueva, M., & MÁ, F.-B. (2020). Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Scientific reports. https://doi.org/10.1038/s41598-020-77296-4
Vancouver
Gómez-Ramírez J, Ávila-Villanueva M, MÁ F.-B. Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Scientific reports. 2020. doi:10.1038/s41598-020-77296-4. PMID: 33244011.
BibTeX
@article{gomezramirez2020-33244011,
author = {Gómez-Ramírez, J. and Ávila-Villanueva, M. and MÁ, Fernández-Blázquez},
title = {{Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods}},
journal = {Scientific reports},
year = {2020},
doi = {10.1038/s41598-020-77296-4},
pmid = {33244011},
url = {https://doi.org/10.1038/s41598-020-77296-4}
}
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