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VOL. 10, ISSUE 1 (2025)
Comparative analysis of tree-based machine learning models for early dementia detection
Authors
Dr. Chizoba Ezeaku Ezeme
Abstract
Early dementia detection is critical for timely
intervention and improving patients’ quality of life. Machine learning (ML) has
emerged as a promising approach to enhance diagnostic accuracy. This study
compares the performance of two prominent tree-based ML models—Random Forest
(RF) and Decision Tree (DT)—in detecting early-stage dementia. Utilizing a
publicly available dataset featuring demographic, genetic, and cognitive
variables, the models were evaluated based on accuracy, recall, precision, and
F1 scores. The results demonstrate that both models perform exceptionally well,
with RF achieving marginally higher metrics due to its ensemble nature. The
study underscores the potential of tree-based models as robust tools for early
dementia prediction.18-02-2025
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Pages:25-27
How to cite this article:
Dr. Chizoba Ezeaku Ezeme "Comparative analysis of tree-based machine learning models for early dementia detection". International Journal of Academic Research and Development, Vol 10, Issue 1, 2025, Pages 25-27
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