# Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
## 4 Related Work  
Several recent studies have explored the use of eye-tracking for the early detection of cognitive impairment, often relying on broader task batteries and higher-end equipment. For instance, [PERSON] et al. [19] introduced a 178-second protocol using high-performance near-infrared eye-tracking to present participants with ten short movie-based tasks assessing different cognitive domains such as memory, attention, and reasoning. The system achieved a classification AUC of 0.845 for distinguishing between HC and MCI and 0.888 for general cognitive impairment (MCI+AD vs HC), with performance comparable to the MMSE. However, the test setup included fixed head positioning and video-based stimuli requiring advanced equipment and controlled testing environments.  
[PERSON] et al. [20] proposed a similarly comprehensive system combining multiple visual stimuli and feature types--including saccades, fixations, smooth pursuit, and pupil size--to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **CA** & **AUC** \\ \hline Logistic regression & 0.619 \(\pm\) 0.174 & **0.675 \(\pm\) 0.236** \\ Decision tree & 0.557 \(\pm\) 0.150 & 0.561 \(\pm\) 0.149 \\ Random forest & **0.628 \(\pm\) 0.152** & 0.625 \(\pm\) 0.219 \\ Naive Bayes with Gaussian likelihood & 0.597 \(\pm\) 0.136 & 0.672 \(\pm\) 0.204 \\ Support vector classifier & 0.530 \(\pm\) 0.142 & 0.603 \(\pm\) 0.182 \\ Majority (dummy) & 0.548 \(\pm\) 0.041 & 0.500 \(\pm\) 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of classification algorithms. Reported values include the mean classification accuracy (CA) and area under the ROC curve (AUC), along with their respective standard deviations.  
Figure 5: (a) ROC curve for the LR model (green line) and for the majority classifier (grey line). (b) A plot presenting each participant sorted by the probability of being classified as impaired. The colours represent their actual classes.  
develop a multi-feature classification model for MCI. Their approach applied advanced feature selection and fusion techniques to maximise discrimination between impaired and healthy participants. Although powerful, such systems are less suited to use outside of research or specialised clinical contexts due to their complexity and resource requirements.  
By contrast, our method was designed to test the lower bound of complexity by using only the smooth pursuit task and consumer-grade hardware. While the resulting classification accuracy is lower than those reported in more complex systems, our approach requires no verbal output, no additional cognitive instructions, and minimal participant burden. These characteristics make it well-suited for scalable and cost-effective screening applications, particularly in non-specialist or telemedicine contexts. Another likely reason why our results show lower accuracy is the presence of borderline subjects in our dataset. They account for almost a third of the dataset and are the most difficult to accurately detect being between HC and MCI groups in the CI continuum. This can also be seen in Figure 4. Of course, detecting individuals in the borderline group would be the most rewarding as this is the earliest possible detection.  
Our findings also align with smaller studies targeting similar goals. For example, [PERSON] et al. [21] found significant differences in smooth pursuit amplitude and fixation variability between AD patients and controls using a simplified pursuit task. Although based on a small sample, their results suggest that even basic oculomotor parameters can offer discriminative value when measured in controlled conditions.