ArticleAmerican journal of Alzheimer's disease and other dementias
Design and Validation of a Hybrid Machine Learning Model for Alzheimer's Detection Using Handwriting Data.
Article in American journal of Alzheimer's disease and other dementias. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Exploring Handwriting-Based Biomarkers for Alzheimer's Disease: Identifying Discriminative Features and Tasks to Enhance Diagnostic Accuracy.Diagnostics (Basel, Switzerland) · 2026Article
- Handwriting speed and pen motor control in older adults with and without cognitive impairment.Frontiers in human neuroscience · 2026Article
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Abstract
Handwriting is a preferred identifier in detecting Alzheimer's disease that enables diagnosis about people. The aim of this study is to evaluate the handwriting and make the early detection and diagnosis of Alzheimer's disease with the highest possible prediction rates. In this regard, 9 machine learning algorithms were used. Seven feature selection methods were used to determine the most effective features for Alzheimer's disease prediction to eliminate unnecessary ones and increase model prediction performance. The models were trained and tested on the DARWIN dataset with both train - test split and cross-validation methods. According to the results, it has been evaluated that the highest performance criterion values are generally achieved when the SHAP is used as the feature selection method. According to the results, the appropriate model that achieved the highest performance values was determined as the hybrid SHAP-Support Vector Machine model with 0.9623 accuracy, 0.9643 precision, 0.9630 recall and 0.9636 F1-Score.
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