Handwriting-Based Classifier Combination for Cognitive Impairment Prediction

Cognitive impairments affect areas such as memory, learning, concentration, or decision making and range from mild to severe. Impairments of this kind can be indicators of neurodegenerative diseases such as Alzheimer’s, that affect millions of people worldwide and whose incidence is expected to increase in the near future. Handwriting is one of the daily activities affected by this kind of impairment, and its anomalies are already used for the diagnosis of neurodegenerative diseases, such as, for example, micrographia in Parkinson’s patients. Classifier combination methods have proved to be an effective tool for increasing the performance in pattern recognition applications. The rationale of this approach follows from the observation that appropriately diverse classifiers, especially when trained on different types of data, tend to make uncorrelated errors. In this paper, we present a study in which the responses of different classifiers, trained on data from graphic tasks, have been combined to predict cognitive impairments. The proposed system has been trained and tested on a dataset containing handwritten traits extracted from some simple graphic tasks, e.g. joining two points or drawing circles. The results confirmed that a simple combination rule, such as the majority vote rule, performs better than single classifiers.