A Multi Classifier Approach for Supporting Alzheimer’s Diagnosis Based on Handwriting Analysis

Abstract

Nowadays, the treatments of neurodegenerative diseases are increasingly sophisticated, mainly thanks to innovations in the medical field. As the effectiveness of care, strategies is enhanced by the early diagnosis, in recent years there has been an increasing interest in developing reliable, non-invasive, easy to administer, and cheap diagnostics tools to support clinicians in the diagnostic processes. Among others, Alzheimer’s disease (AD) has received special attention in that it is a severe and progressive neurodegenerative disease that heavily influence the patient’s quality of life, as well as the social costs for proper care. In this context, a large variety of methods have been proposed that exploit handwriting and drawing tasks to discriminate between healthy subjects and AD patients. Most, if not all, of these methods adopt a single machine learning technique to achieve the final classification. We propose to tackle the problem by adopting a multi-classifier approach envisaging as many classifiers as the number of tasks, each of whom produces a binary output. The outputs of the classifiers are eventually combined by a majority vote to achieve the final decision. Experiments on a dataset involving 175 subjects executing 25 different handwriting and drawing tasks and 6 different machine learning techniques selected among the most used ones in the literature show that the best results are achieved by selecting the subset of tasks on which each classifier perform best and then combining the outputs of the classifier on each task, achieving an overall accuracy of 91% with a sensitivity of 83% and a specificity of 100%. Moreover, this strategy reduces the meantime to complete the test from 25 minutes to less than 10.