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Improving Early Detection of Dementia: Extra Trees-Based Classification Model Using Inter-Relation-Based Features and K-Means Synthetic Minority Oversampling Technique
Yanawut Chaiyo, Worasak Rueangsirarak, Georgi Hristov, Punnarumol Temdee. Big Data and Cognitive Computing, 2025-05-30
Topic: Risk, prevention and care in population studies
The early detection of dementia, a condition affecting both individuals and society, is essential for its effective management. However, reliance on advanced laboratory tests and specialized expertise limits accessibility, hindering timely diagnosis. To address this challenge, this study proposes a novel approach in which readily available biochemical and physiological features from electronic health records are employed to develop a machine learning-based binary classification model, improving accessibility and early detection. A dataset of 14,763 records from Phachanukroh Hospital, Chiang Rai, Thailand, was used for model construction. The use of a hybrid data enrichment framework involving feature augmentation and data balancing was proposed in order to increase the dimensionality of the data. Medical domain knowledge was used to generate inter-relation-based features (IRFs), which improve data diversity and promote explainability by making the features more informative. For data balancing, the K-Means Synthetic Minority Oversampling Technique (K-Means SMOTE) was applied to generate synthetic samples in under-represented regions of the feature space, addressing class imbalance. Extra Trees (ET) was used for model construction due to its noise resilience and ability to manage multicollinearity. The performance of the proposed method was compared with that of Support Vector Machine, K-Nearest Neighbors, Artificial Neural Networks, Random Forest, and Gradient Boosting. The results reveal that the ET model significantly outperformed other models on the combined dataset with four IRFs and K-Means SMOTE across key metrics, including accuracy (96.47%), precision (94.79%), recall (97.86%), F1 score (96.30%), and area under the receiver operating characteristic curve (99.51%).
Key findings
- The performance of the proposed method was compared with that of Support Vector Machine, K-Nearest Neighbors, Artificial Neural Networks, Random Forest, and Gradient Boosting.
- The results reveal that the ET model significantly outperformed other models on the combined dataset with four IRFs and K-Means SMOTE across key metrics, including accuracy (96.47%), precision (94.79%), recall (97.86%), F1 score (96.30%), and area under the receiver operating characteristic curve (99.51%).
Why it matters
This study built a machine learning model using routine biochemical and physiological features from electronic health records to classify dementia, and it performed well on key metrics compared with other models. It is a model-development study, so it does not show that using this tool improves diagnosis or outcomes in real care.
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Study details
- Effect size96.47%; 94.79%; 97.86%
- Evidence strengthLow
How this study scored 35/100
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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Big Data and Cognitive Computing) |
| Journal's citation record | 0 / 4 | Big Data and Cognitive Computing: journal h-index 70 (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 | 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 | 1 / 7 | Cited 4 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.0× 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 | 1 / 4 | Cited 4 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Punnarumol Temdee: h-index 15 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Mae Fah Luang University: institution h-index 321 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 35 / 100 | — |
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Read the original
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
Chaiyo, Y., Rueangsirarak, W., Hristov, G., & Temdee, P. (2025). Improving Early Detection of Dementia: Extra Trees-Based Classification Model Using Inter-Relation-Based Features and K-Means Synthetic Minority Oversampling Technique. Big Data and Cognitive Computing. https://doi.org/10.3390/bdcc9060148
Vancouver
Chaiyo Y, Rueangsirarak W, Hristov G, Temdee P. Improving Early Detection of Dementia: Extra Trees-Based Classification Model Using Inter-Relation-Based Features and K-Means Synthetic Minority Oversampling Technique. Big Data and Cognitive Computing. 2025. doi:10.3390/bdcc9060148.
BibTeX
@article{chaiyo2025-909dadd9,
author = {Chaiyo, Yanawut and Rueangsirarak, Worasak and Hristov, Georgi and Temdee, Punnarumol},
title = {{Improving Early Detection of Dementia: Extra Trees-Based Classification Model Using Inter-Relation-Based Features and K-Means Synthetic Minority Oversampling Technique}},
journal = {Big Data and Cognitive Computing},
year = {2025},
doi = {10.3390/bdcc9060148},
url = {https://doi.org/10.3390/bdcc9060148}
}
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