Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework

Alzheimer’s disease (AD) is a persistent neurologic disorder that has no cure. For a successful treatment to be implemented, it is essential to diagnose AD at an early stage, which may occur up to eight years before dementia manifests. In this regard, a new predictive machine learning model is proposed that works in two stages and takes advantage of both unsupervised and supervised learning approaches to provide a fast, affordable, yet accurate solution. The first stage involved fuzzy partitioning of a gold-standard dataset, DARWIN (Diagnosis AlzheimeR WIth haNdwriting). This dataset consists of clinical features and is designed to detect Alzheimer’s disease through handwriting analysis. To determine the optimal number of clusters, four Clustering Validity Indices (CVIs) were averaged, which we refer to as cognitive features. During the second stage, a predictive model was constructed exclusively from these cognitive features. In comparison to models relying on datasets featuring clinical attributes, models incorporating cognitive features showed substantial performance enhancements, ranging from 12% to 26%. Our proposed model surpassed all current state-of-the-art models, achieving a mean accuracy of 99%, mean sensitivity of 98%, mean specificity of 100%, mean precision of 100%, and mean MCC and Cohen’s Kappa of 98%, along with a mean AUC-ROC score of 99%. Hence, integrating the output of unsupervised learning into supervised machine learning models significantly improved their performance. In the process of crafting early interventions for individuals with a heightened risk of disease onset, our prognostic framework can aid in both the recruitment and advancement of clinical trials.