ArticleHealthcare technology letters
Writing the Signs: An Explainable Machine Learning Approach for Alzheimer's Disease Classification from Handwriting.
Article in Healthcare technology letters. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Handwriting as a Biomarker for Early Detection of Parkinson's and Alzheimer's Diseases: A Comprehensive Guide for Researchers.Sensors (Basel, Switzerland) · 2026Review
- Exploring Handwriting-Based Biomarkers for Alzheimer's Disease: Identifying Discriminative Features and Tasks to Enhance Diagnostic Accuracy.Diagnostics (Basel, Switzerland) · 2026Article
- MCI detection from handwritten drawing test using residual vision transformer.Scientific reports · 2026Article
- Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics.JMIR aging · 2025Article
- Design and Validation of a Hybrid Machine Learning Model for Alzheimer's Detection Using Handwriting Data.American journal of Alzheimer's disease and other dementiasArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease is a global health challenge, emphasizing the need for early detection to enable timely intervention and improve outcomes. This study analyzes handwriting data from individuals with and without Alzheimer's to identify predictive features across copying, graphic and memory-based tasks. Machine learning models, including Random Forest, Bootstrap Aggregating (Bagging), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost) and Gradient Boosting, were applied to classify patients, with SHapley Additive exPlanations (SHAP) enhancing model interpretability. Time-related features were crucial in copying and graphic tasks, reflecting cognitive processing speed, while pressure-related features were significant in memory tasks, indicating recall confidence. Simpler graphic tasks showed strong discriminatory power, aiding early detection. Performance metrics demonstrated model effectiveness: For memory tasks, Random Forest achieved the highest accuracy (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.