A Novel Feedforward Neural Network Model for Accurate Detection of Alzheimer's Disease Using Handwriting Analysis
TL;DR
An interpretable deep learning framework for AD diagnosis using the DARWIN handwriting dataset is proposed, which integrates Recursive Feature Elimination with a Feedforward Neural Network (FNN) to identify the most discriminative handwriting features, reduce dimensionality, and enhance generalization.
Abstract
Dementia, particularly Alzheimer’s disease (AD), poses a major global health challenge, currently affecting over 55 million people and projected to reach 152 million by 2050. While neuroimaging-based approaches have demonstrated high diagnostic performance, there is growing interest in inexpensive, non-invasive alternatives such as handwritingderived digital biomarkers. Handwriting analysis, as provided by the DARWIN dataset, can capture subtle motor and cognitive decline using widely available digital devices. Recent machine learning (ML) and DL studies on handwriting-based AD detection show promise but often rely on complex architectures, large datasets, and provide limited transparency regarding feature importance. In this work, we propose an interpretable deep learning framework for AD diagnosis using the DARWIN handwriting dataset. Our approach integrates Recursive Feature Elimination (RFE) with a Feedforward Neural Network (FNN) to identify the most discriminative handwriting features, reduce dimensionality, and enhance generalization. Remarkably, the model achieves strong performance with only 20 selected features, demonstrating robustness in small-sample learning scenarios, as evidenced by a 10-fold cross-validation accuracy of 0.9833 ± 0.0356, ROC-AUC of 0.9987 ± 0.0039, F1-score of 0.9841 ± 0.0331, recall of 0.9818 ± 0.0545, and precision of 0.9889 ± 0.0333. Unlike data-intensive CNN, RNN, or Transformer models, the FNN attains perfect accuracy, sensitivity, and precision on DARWIN while remaining lightweight and scalable. To strengthen clinical trust, we incorporate SHAP-based explainability to highlight critical handwriting biomarkers and optimize assessment protocols by identifying less informative tasks. Comparative evaluations against established ML and DL baselines confirm consistent performance gains and enhanced interpretability, underscoring the potential of handwriting-based approaches for practical and transparent early AD detection.