A Systematic Review of Explainable NLP-based Handwriting Techniques for Early Prediction of Alzheimer’s Disease

Abstract

Initial identification of Alzheimer’s disease (AD) a progressive neurological illness can greatly enhance patient care. Alternative approaches are necessary since traditional diagnostic techniques like neuroimaging are expensive, intrusive, and less accessible. This research work reviews the literature of existing studies covering 2018–2025 and provides a thorough literature review analysis on explainable handwriting approaches based on Natural Language Processing (NLP) for the early prediction of Alzheimer’s disease . The review highlights that stroke velocity, pressure variation, spatial consistency and temporal pauses are very important motor biomarkers of cognitive decline in handwriting. it also states that lexical richness, syntactic complexity, and semantic coherence are linguistic features which when examined through NLP driven textual analysis corroborate these findings. When it comes to distinguishing AD from control groups, machine learning and deep learning models such as Random Forest, XGBoost, Convolutional Neural Network (CNN), and transformer-based architectures have reached accuracies above 90%. Significantly, clinical interpretability has been improved with the use of Explainable Artificial Intelligence (XAI) approaches like Gradient-weighted Class Activation Mapping (Grad-CAM), SHapley Additive exPlanations (SHAP), and cal Interpretable Model-agnostic Explanations (LIME), which enable the display of diagnostic reasoning at the feature level. There are still some unanswered questions about standardized explainability frameworks, cross-population generalizability, and dataset heterogeneity, notwithstanding these advances. In conclusion, this analysis highlights the promising future of explainable NLP-handwriting systems as a means to bridge computational innovation with clinical application; these systems might be used as non-invasive, interpretable, and cost-effective diagnostic methods for Alzheimer’s disease.