Interpreting acoustic features for the assessment of Alzheimer’s disease using ForestNet

Abstract

Nowadays, interpretable machine learning models are one of the most critical topics in the medical domain. The lack of interpretation leads to blind and unreliable models for clinicians, despite the fact that the aim is to support diagnosis through these tools. This problem has been increasing since the creation of large models such as those based on deep learning, which, despite providing good performance in prediction and classification tasks, are not transparent to human understanding. One of the increasingly prevalent clinical problems related to acoustic and linguistic disorders is Alzheimer’s disease (AD), where one important challenge is to provide speech markers that help in supporting, understanding, and facilitating the diagnosis and monitoring of the disease. It motivates this study which proposes a methodology focused on analyzing acoustic features in AD and at the same time providing interpretation from the results. The proposed approach consists of using decision tree-based methods together with neural networks (ForestNet) for analyzing the classification results. Only features that can give interpretation were considered. Unweighted average recalls of up to 79% were achieved for discriminating AD patients. Then, we looked at the relevant features that provided most of the information for assessing AD, which were those related to rhythm, voiced rates, duration, and phone rates. This confirms that this kind of approach can be suitable for the discrimination of AD while maintaining a good performance. • An approach based on decision trees together with artificial neural networks (ForesNet) is considered for analyzing/interpreting different sets of acoustic features for the assessment of Alzheimer’s disease. • Unweighted average recalls of up to 79% were achieved for discriminating Alzheimer’s patients. • The proposed approach outperformed the use of regular decision tree-based methods by 11%. • The considered methods and results here may be interpretative from the clinical point of view, which makes our model suitable for supporting the clinical diagnosis of Alzheimer’s disease.