A System for Aiding Diagnosis of Alzheimer’s Disease and Related Disorders with an Adaptable Decision Model

Aging is a worldwide phenomenon and represents a growing concern for public health systems. In this context, neurodegenerative diseases like Alzheimer’s Disease (AD) have a high prevalence among the elderly. Early diagnosis of AD allows early treatment and improves patient’s quality of life. In this paper, we propose a clinical decision support system (CDSS) to aid physicians in diagnosis of AD and related disorders: Dementia (D) and Mild Cognitive Impairment (MCI). For each case, the system exhibits the most probable diagnosis, the most relevant health records and unobserved health records that could confirm such diagnosis. Moreover, the system has the ability to refine its decision model using the final diagnosis reported by physicians. The system can offer to physicians a friendly user interface designed for smartphones. The proposed decision support system is flexible and adaptable to different contexts, since it allows new neuropsychological tests to be included and its decision model to be adapted automatically. Clinical cases from Center for Alzheimer’s Disease (CAD) of Federal University of Rio de Janeiro (UFRJ) were used as the training dataset for the proposed supervised learning method. Preliminary tests using clinical cases from Antônio Pedro University Hospital (HUAP) of Fluminense Federal University (UFF) showed that the proposed CDSS decision model achieves good performance (accuracy of 0.94 for D and 0.85 for MCI) by a computational method that evaluates several classifiers and selects the best for each mental disorder.