Diagnostic Potential of Eye Movements in Alzheimer’s Disease via a Multiclass Machine Learning Model

Abstract

Early diagnosis plays a crucial role in controlling Alzheimer’s disease (AD) progression and delaying cognitive decline. Traditional diagnostic tools present great challenges to clinical practice due to their invasiveness, high cost, and time-consuming administration. This study was designed to construct a non-invasive and cost-effective classification model based on eye movement parameters to distinguish dementia due to AD (ADD), mild cognitive impairment (MCI), and normal cognition. Eye movement data were collected from 258 subjects, comprising 111 patients with ADD, 81 patients with MCI, and 66 individuals with normal cognition. The fixation, smooth pursuit, prosaccade, and anti-saccade tasks were performed. Machine learning methods were used to screen eye movement parameters and build diagnostic models. Pearson’s correlation analysis was used to assess the correlations between the five most important eye movement indicators in the optimal model and neuropsychological scales. The gradient boosting classifier model demonstrated the best classification performance, achieving 68.2% of accuracy and 66.32% of F1-score in multiclass classification of AD. Moreover, the correlation analysis indicated that the eye movement parameters were associated with various cognitive functions, including general cognitive status, attention, visuospatial ability, episodic memory, short-term memory, and language and instrumental activities of daily life. Eye movement parameters in conjunction with machine learning methods achieve satisfactory overall accuracy, making it an effective and less time-consuming method to assist clinical diagnosis of AD.