Enhanced Multi-Model Machine Learning-Based Dementia Detection Using a Data Enrichment Framework: Leveraging the Blessing of Dimensionality

Topic: Risk, prevention and care in population studies

The early diagnosis of dementia, a progressive condition impairing memory, cognition, and functional ability in older adults, is essential for timely intervention and improved patient outcomes. This study proposes a novel multiclass classification that differentiates dementia from other comorbid conditions, specifically cardiovascular diseases, including heart failure and aortic valve disorder, by leveraging the "blessing of dimensionality" to enhance predictive performance while ensuring feature accessibility. Using a dataset of 26,474 electronic health records from two hospitals in Chiang Rai, Thailand, the proposed framework introduced clinically informed feature augmentation to enhance model generalizability. Furthermore, the borderline synthetic minority oversampling technique was employed to address class imbalance, enhancing the model's performance for minority classes. This study systematically evaluated a suite of machine learning models, including extreme gradient boosting, gradient boosting, random forest, support vector machine, decision trees, k-nearest neighbors, extra trees, and TabNet, across both the original and enriched datasets, with the latter integrating augmented features and synthetic data. Predictive performance was assessed using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and area under the precision-recall curve. The results revealed that all the models exhibited consistent performance improvements with the enriched dataset, affirming the value of dimensionality when guided by domain expertise.

Key findings

• The results revealed that all the models exhibited consistent performance improvements with the enriched dataset, affirming the value of dimensionality when guided by domain expertise.

Why it matters

Using electronic health records from two hospitals in Thailand, this study found that adding clinically informed features and synthetic data improved machine learning models that distinguish dementia from conditions such as heart failure and aortic valve disorder. It is about detection methods, not treatment, cure or prevention.

New to this subject? Start with our plain-English guide: Digital health →

Study details

• Evidence strengthLow

Read the original

• DOI: 10.3390/bioengineering12060592
• PubMed: 40564409
• Original source →
• Free full text: Bioengineering →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{yongcharoenchaiyasit2025-40564409,
  author  = {Yongcharoenchaiyasit, Khomkrit and Arwatchananukul, Sujitra and Hristov, Georgi and Temdee, Punnarumol},
  title   = {{Enhanced Multi-Model Machine Learning-Based Dementia Detection Using a Data Enrichment Framework: Leveraging the Blessing of Dimensionality}},
  journal = {Bioengineering},
  year    = {2025},
  doi     = {10.3390/bioengineering12060592},
  pmid    = {40564409},
  url     = {https://doi.org/10.3390/bioengineering12060592}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Diagnosis and Management of Dementia: Review JAMA · 2019-10-01 · usefulness score 59/100 · Getting a diagnosis
• Age-Related Diseases and Clinical and Public Health Implications for the 85 Years Old and Over Population Frontiers in public health · 2017-12-11 · usefulness score 51/100 · Caregiving
• Heart disease as a risk factor for dementia Clinical epidemiology · 2013-04-26 · usefulness score 46/100 · Prevention and risk

Related studies in Digital health tools

• Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning SAR and QSAR in environmental research · 2025-02-01 · usefulness score 34/100
• Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data PloS one · 2013-05-31 · usefulness score 52/100
• Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer's Disease Using Structural MRI Features Frontiers in neurology · 2019-10-25 · usefulness score 46/100
• Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model International journal of molecular sciences · 2020-09-21 · usefulness score 49/100
• Enhanced diagnostic interpretation of the MoCA using machine learning Frontiers in Neuroscience · 2026-02-20 · usefulness score 46/100
• An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT Ophthalmology Science · 2026-01-10 · usefulness score 54/100

Similar studies

• Multi-model deep learning for dementia detection: addressing data and model limitations 2025 · Getting a diagnosis
• Machine learning algorithms and traditional statistical models for detection of dementia: a population-based study 2025 · Digital health tools
• Face Mesh for Dementia Detection: Evaluating Data Augmentation in Deep Learning and Traditional Machine Learning 2025 · Digital health tools
• Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems 2025 · Digital health tools
• Identifying incident dementia by applying machine learning to a very large administrative claims dataset 2019 · Digital health tools
• Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study 2018 · Digital health tools

More on Digital health tools

• A review that covers this ground Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review JAMIA open · 2021-07-01 · usefulness score 44/100
• Newest in this subject Speaker-Wise Dementia Screening fromNaturalistic Speech with Lexical Entropy, Semantic Drift, and Multimodal Fusion Research Square · 2026-10-21 · Preprint — not yet peer-reviewed · usefulness score 16/100
• Newest in this subject Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer’s Disease Drug Discovery bioRxiv · 2026-10-20 · Preprint — not yet peer-reviewed · usefulness score 16/100

See all studies in Digital health tools →