Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease
2024
Comparison of Ensemble Techniques for Early Prediction of Alzheimer's Disease
publication
Author Information
Author(s): Orlunwo Placida Orochi, Onuodu Friday Eleonu
Hypothesis
Can ensemble learning techniques effectively predict the transition from mild cognitive impairment to probable Alzheimer's disease?
Conclusion
The adaptive boost, stacking, and bagging ensemble approaches can identify individuals at risk of developing Alzheimer's disease.
Supporting Evidence
- The study utilized data from the Alzheimer's Disease Neuroimaging Initiative cohort.
- Early detection of Alzheimer's disease is crucial for effective treatment.
Takeaway
This study looks at different methods to predict if someone with mild cognitive issues might develop Alzheimer's disease, which could help them get treatment sooner.
Methodology
Five ensemble learning approaches were used, incorporating neuroimaging biomarkers, cerebrospinal fluid, and cognitive performance data.
Digital Object Identifier (DOI)
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