Eye tracking has shown promise in detecting group-wise differences between healthy controls and people with Mild Cognitive Impairment (MCI) who might progress to develop Alzheimer's disease. As there is currently no cure, only medications to slow the progress of the disease, it is of paramount importance to find the persons at risk early.

We extracted saccade- and Region of Interest (ROI)-centric feature sets from a clinical eye tracking dataset and analyzed the results using machine learning to establish which features were most beneficial to correctly classify individuals at risk.

Our results show that the analysis of multimodal eye tracking recordings of number and text reading tasks produces feature sets that can have good predictive value to classify MCIs from healthy controls. Changes in participants' pupil sizes from the personalized baseline appear to be especially promising candidates for improving the classification efficiency of MCIs.