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Dementia01:30

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Enhanced Multi-Model Machine Learning-Based Dementia Detection Using a Data Enrichment Framework: Leveraging the

Khomkrit Yongcharoenchaiyasit1,2, Sujitra Arwatchananukul2, Georgi Hristov3

  • 1Computer and Communication Engineering for Capacity Building Research Center, Chiang Rai 57100, Thailand.

Bioengineering (Basel, Switzerland)
|June 26, 2025
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Summary
This summary is machine-generated.

This study enhances dementia diagnosis by differentiating it from cardiovascular diseases using machine learning and feature engineering. Enriched datasets improved all models, highlighting the value of dimensionality in clinical predictions.

Keywords:
aortic valve disorderblessing of dimensionalitydementiafeature augmentationheart failureoversampling technique

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Area of Science:

  • Computational medicine and health informatics.
  • Machine learning applications in clinical diagnostics.

Background:

  • Early dementia diagnosis is crucial for effective intervention in older adults.
  • Dementia often co-occurs with cardiovascular diseases, complicating diagnosis.
  • Existing diagnostic methods may struggle to differentiate these conditions effectively.

Purpose of the Study:

  • To develop a multiclass classification framework to distinguish dementia from cardiovascular comorbidities (heart failure, aortic valve disorder).
  • To leverage "blessing of dimensionality" for improved predictive performance and feature accessibility.
  • To enhance model generalizability and performance on minority classes.

Main Methods:

  • Utilized a dataset of 26,474 electronic health records from Thai hospitals.
  • Employed clinically informed feature augmentation and the borderline synthetic minority oversampling technique (SMOTE) for data enrichment and class imbalance.
  • Evaluated multiple machine learning models (e.g., XGBoost, Random Forest, TabNet) on original and enriched datasets using standard performance metrics.

Main Results:

  • All evaluated machine learning models demonstrated consistent performance improvements on the enriched dataset compared to the original.
  • Feature augmentation and SMOTE effectively enhanced model generalizability and minority class performance.
  • The study confirmed the benefits of increased dimensionality, guided by domain expertise, for diagnostic accuracy.

Conclusions:

  • The proposed framework effectively differentiates dementia from cardiovascular diseases.
  • Data enrichment strategies significantly boost the performance of machine learning models for complex clinical diagnoses.
  • Leveraging dimensionality with clinical insights offers a promising avenue for improving early dementia detection.