Bridging the Gap: Missing Data Imputation Methods and Their Effect on Dementia Classification Performance

Topic: Risk, prevention and care in population studies

On the test set, MICE imputation yielded the highest accuracy for both RF (0.76) and LR (0.81), while SVM performed best with median imputation (0.81). McNemar’s test revealed significant differences between RF and both LR and SVM (p < 0.01), but not between LR and SVM. Simpler methods like mean and median performed adequately but were generally outperformed by MICE. The performance of kNNs and MF was less consistent. Overall, the choice of imputation method significantly affects classification accuracy. Selecting strategies tailored to both data structure and classifier is essential for robust predictive modeling in clinical neuroscience.

Key findings

• On the test set, MICE imputation yielded the highest accuracy for both RF (0.76) and LR (0.81), while SVM performed best with median imputation (0.81).
• McNemar’s test revealed significant differences between RF and both LR and SVM (p < 0.01), but not between LR and SVM.
• Simpler methods like mean and median performed adequately but were generally outperformed by MICE.
• The performance of kNNs and MF was less consistent.
• Overall, the choice of imputation method significantly affects classification accuracy.

Why it matters

This study compared ways of filling in missing data and found that the choice of method significantly affects how accurately dementia is classified, with MICE generally performing best. It is a methods study and does not address treating, curing or preventing Alzheimer's disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.3390/brainsci15060639
• PubMed: 40563810
• Original source →
• Free full text: Brain Sciences →

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

@article{aracri2025-40563810,
  author  = {Aracri, Federica and Bianco, Maria Giovanna and Quattrone, Andrea and Sarica, Alessia},
  title   = {{Bridging the Gap: Missing Data Imputation Methods and Their Effect on Dementia Classification Performance}},
  journal = {Brain Sciences},
  year    = {2025},
  doi     = {10.3390/brainsci15060639},
  pmid    = {40563810},
  url     = {https://doi.org/10.3390/brainsci15060639}
}

Studies this one cites

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

• Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization Neurology · 2009-12-30 · usefulness score 68/100 · Mild cognitive impairment
• Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review Frontiers in aging neuroscience · 2017-10-06 · usefulness score 68/100 · Brain scans and imaging
• MRI Asymmetry Index of Hippocampal Subfields Increases Through the Continuum From the Mild Cognitive Impairment to the Alzheimer's Disease Frontiers in neuroscience · 2018-08-21 · usefulness score 46/100 · Mild cognitive impairment

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• Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods Diagnostics (Basel, Switzerland) · 2021-08-09 · usefulness score 50/100
• Cognitive performance classification of older patients using machine learning and electronic medical records Scientific Reports · 2025-02-24 · usefulness score 49/100
• A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction Scientific reports · 2020-11-23 · usefulness score 67/100
• Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data Frontiers in aging neuroscience · 2019-08-20 · usefulness score 72/100

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