A Bayesian network combiner for multimodal handwriting analysis in Alzheimer’s disease detection

Abstract

Alzheimer’s disease, recognized as the most widespread neurodegenerative disorder worldwide, strongly affects the cognitive ability of patients. The cognitive impairments range from mild to severe and are a risk factor for Alzheimer’s disease. They have profound implications for individuals, even as they maintain some daily functionality. Previous studies proposed a protocol involving handwriting tasks as a potential diagnostic tool for predicting the symptoms of Alzheimer’s disease. Literature reveals that the potential of multimodal handwriting analysis, leveraging data from multiple handwriting tasks, has not been fully explored. Thus, we propose a two-stage multimodal approach for Alzheimer’s disease detection using handwriting data derived from the protocol mentioned above, including 25 tasks. In the first stage, static and dynamic handwriting features are extracted and fused with the subject’s personal information. Then, the data obtained for each task are used to train a single classifier, providing task-specific predictions. Thus, for each subject, 25 different predictions are provided by the whole system. In the second stage, a Bayesian Network is used to model task interdependencies and to select, via the Markov Blanket, the task subset conditionally dependent on the class label. The experimental findings demonstrate that the proposed multimodal combining classifiers approach outperforms single-task classifiers and other ensemble methods. The proposed approach achieved the highest accuracy (86.98%) by using the Majority Vote method for the tasks included in the Markov Blanket selection.