Towards Early Detection of Mild Cognitive Impairment: Predictive

Analytics Using the Oculo-Cognitive Addition Test (OCAT)

Gaurav N. Pradhan

1,2 a

, Sarah E. Kingsbury

, Michael J. Cevette

, Jan Stepanek

and Richard J. Caselli

Department of Biomedical Informatics, Mayo Clinic, Scottsdale, Arizona, U.S.A.

Department of Otolaryngology-Head and Neck Surgery, Division of Audiology, Mayo Clinic, Scottsdale, Arizona, U.S.A.

Aerospace Medicine Program, Department of Internal Medicine, Mayo Clinic, Scottsdale, Arizona, U.S.A.

Department of Neurology, Mayo Clinic, Scottsdale, Arizona, U.S.A.

Keywords: Mild Cognitive Impairment, Cognitive Decline, Cognitive Performance, Oculometrics, Prediction, Classification.

Abstract: Mild cognitive impairment (MCI) is often challenging to diagnose. The Oculo-Cognitive Addition Test

(OCAT) is a rapid, objective tool that measures eye movement and time-based features during mental addition

tasks in under one minute. This study aims to develop predictive machine learning algorithms for early

detection of those at greater risk for mild cognitive impairment, helping warrant further testing. OCAT testing

with integrated eye tracking was completed by 250 patients. Time-related and eye movement features were

extracted from raw gaze data. Feature selection was performed using machine learning methods, including

random forest and univariate decision trees, to identify predictors of Dementia Rating Scale (DRS) outcomes.

Supervised models—logistic regression (LR) and K-nearest neighbors (KNN)—were trained to classify MCI.

Class imbalance was addressed using the Synthetic Minority Over-sampling Technique. LR models achieved

the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall

of 0.91, and the area under the precision-recall curve (AUPRC) of 0.95. This study demonstrates that machine

learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering

a promising approach for early identification of MCI.

1 INTRODUCTION

Mild Cognitive Impairment (MCI) is a condition that

represents a transitional state between normal aging

and dementia, particularly Alzheimer’s Disease (AD)

(Anderson, 2019; Chen et al., 2021; Petersen et al.,

1999), though some cases stem from reversible

causes such as concussion, metabolic pathologies,

and psychiatric disorders (Petersen et al., 2018).

According to the 2018 American Academy of

Neurology guidelines, individuals with MCI exhibit

cognitive deficits while maintaining functional

independence in activities of daily living (Petersen et

al., 2018). Diagnostic criteria for MCI have evolved

over time – from initially emphasizing memory loss

(Petersen et al., 1999) to including impairments in

other domains such as language, visuospatial

processing, or executive skills (Chen et al., 2021;

https://orcid.org/0000-0002-4040-462X

https://orcid.org/0009-0004-3411-242X

Petersen, 2004) – reflecting the heterogeneity of its

clinical presentation.

When cognitive decline or MCI is suspected, a

comprehensive neuropsychological assessment

remains the gold standard for diagnosis (Aarsland et

al., 2009; Matteau et al., 2011). However, due to

testing time, rigor, clinical availability, and insurance

coverage, not all patients can undergo full testing. As

a validated alternative, the Mattis Dementia Rating

Scale (DRS) is frequently used to measure general

cognitive function (Matteau et al., 2011; Porto et al.,

2007). The DRS has also been validated as a short-

form test for MCI screening (Matteau et al., 2011).

Even so, this test can take as few as 15-20 minutes

with an alert, healthy individual, but can take close to

an hour for a person with advanced impairment

(Marson et al., 1997; Matteau et al., 2011).

Pradhan, G. N., Kingsbury, S. E., Cevette, M. J., Stepanek, J. and Caselli, R. J.

Towards Early Detection of Mild Cognitive Impairment: Predictive Analytics Using the Oculo-Cognitive Addition Test (OCAT).

DOI: 10.5220/0013808300004000

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 1: KDIR, pages 457-464

457

Given the time and resource demands of

comprehensive neuropsychological assessment, there

is a clinical need for a brief, objective screening tool

to predict a patient’s relative risk for MCI, ensuring

that those at greatest need are prioritized for further

assessment. It has been shown that patients with a

score lower than 140 on the DRS are at greater

likelihood of having MCI (Harvey & Mohs, 2001;

Mattis, 1976; Montgomery, 1982; Schmidt et al.,

1994). Note this cutoff is much higher than the

clinical psychometric threshold of 123 (Mattis, 1988).

Many neurological diseases lead to changes in eye

movements, also termed oculometrics, and these

changes have been identified as early biomarkers of

Alzheimer’s disease and related dementias (Laguna et

al., 2011; Mosimann et al., 2005). Studies have shown

that cognitive impairment can be identified by

quantifying changes in oculometric patterns such as

fixation duration, saccadic velocity, blinks, and

pupillary response (Pradhan et al., 2019; Pradhan et

al., 2018; Pradhan et al., 2022). The Oculo-Cognitive

Addition Test (OCAT) (Pradhan et al., 2024) was

designed to efficiently (< 2 minutes) assess cognitive

function (Pradhan et al., 2022) by capturing eye

movements and associated time parameters during

verbally administered mental addition tasks.

Participants are instructed to complete, as rapidly as

possible, 12 trials of summing three consecutive

numbers shown separately on three consecutive blank

screens in seemingly random positions categorized by

low, medium, and high cognitive workload,

respectively. While the location of the numbers may

appear random to the subject, OCAT consists of a

structured “infinity loop” pattern of 24 symmetrical

positions in which the numbers appear (Figure 1). By

utilizing this “infinity loop” pattern, OCAT is

inherently structured, allowing for measurement and

quantification of eye movements in horizontal,

vertical, and diagonal directions, using integrated

eye-tracking hardware, while modulating cognitive

demand through verbal addition tasks, to engage

brain areas associated with attention, working

memory, numerical representation, and oculometric

coordination (Pradhan et al., 2022). Eye movement

features measured include saccades, fixations, blinks,

and pupil dilations, which have a variety of functions

with multiple voluntary and reflexive factors (Kang

et al., 2023; Purves et al., 2001). Time-based features

recorded—such as total test time, the average time to

complete each three-number addition task (where

each number is associated with the low, medium, or

high cognitive load), and response time of following

the number on the screen also reflect cognitive

processes like attention, processing speed, and

working memory. In a proof of concept study, OCAT

demonstrated increased saccadic latency and fixation

time in a hypoxic population (Pradhan et al., 2022),

supporting its sensitivity to cognitive load.

The objective of this study was to develop

predictive machine learning models for possible mild

cognitive impairment (PMCI) identification using the

combined eye movement and time-based features

extracted from the raw gaze data during the OCAT

testing. OCAT was performed the same day as, but

prior to, neuropsychological testing which included

the DRS. It is hypothesized that with the optimal

model, the features derived from OCAT will reliably

and accurately predict DRS outcomes (PASS/FAIL).

This study represents the foundational work toward

establishing OCAT as a rapid, objective screening

tool for neurological function, with the potential to

guide further neuropsychological assessment.

Figure 1: Eye tracking data during OCAT showing the

fixations (in red) and saccadic movement (in blue).

2 METHODOLOGY

2.1 Participants

250 participants who underwent neuropsychological

testing were enrolled in this study protocol, which

was approved by the Institutional Review Board

(IRB). Subjects between 21 and 99 years of age with

normal vision (including those with correction), no

clinically significant visual impairment, and the

ability to provide consent themselves were enrolled.

Informed consent was obtained from all participants

before enrolment. All tested participants were

categorized into Cognitive Normal (CN) and Possible

Mild Cognitive Impairment (PMCI) groups,

including those with mild dementia, based on their

DRS score during clinical neuropsychological

assessment. As per the clinical standards, participants

with a DRS score of 140 or above were labelled as

CN, and those with less than 140 scores were labelled

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458

as PMCI (Harvey & Mohs, 2001). A portion of the

data was used to train and optimize supervised

machine learning models, while remaining data was

reserved for independent validation. Model

performance was evaluated by assessing recall,

specificity, precision, and accuracy in classifying

patients with DRS scores indicative of MCI, based on

established diagnostic thresholds (Harvey & Mohs,

2001). Participants whose eye-tracking data during

OCAT testing did not meet the predefined “tracking

ratio” threshold of 80% due to excessive signal loss,

poor eye-tracking calibration, or unstable gaze

recordings were excluded from further analysis to

ensure the reliability and validity of gaze-based

cognitive performance measurements during the

OCAT. The final distributed dataset included the

OCAT data of 206 participants, with 166 categorized

as CN class and 40 as PMCI class.

2.2 Equipment and Procedures

The OCAT testing was conducted in a quiet, climate-

controlled room. The ambient light levels were at

stable luminance and consistent throughout the data

collection. The OCAT software was installed on the

14” EyeOn Elite Windows 10 Pro tablet (EyeTech

Digital Systems, Tempe, Arizona, USA) with an

integrated eye tracking device (8MP Eye Gaze

Camera) to track eye gaze during the OCAT testing.

The resolution of the tablet screen was 1920 x 1080.

The raw eye-gaze data was collected at a sampling

rate of 120 Hz. During the study, participants were

seated facing the tablet and positioned to maintain a

viewing distance of 60 cm, a distance within the

recommended reading range. The session began with

a 5-point calibration to optimize eye-tracking

accuracy. Calibration quality was verified, and

recalibration was performed if necessary to maintain

a calibration error below 0.5 degrees. To control

potential task novelty effects, a structured preview

and practice phase were incorporated before data

acquisition. Participants first received standardized

instructions and observed a demonstration of the task.

Subsequently, an initial practice session was

conducted, during which participants completed a full

OCAT trial that allowed for task familiarization and

reduction of learning-related variability. The

subsequent OCAT trial was considered the formal

test, and only data from this test were analyzed. The

OCAT testing session was performed before any

scheduled neuropsychological testing for the

Neurology appointment to avoid any fatigue effects.

2.3 Data Processing and Feature

Extraction

The time-related features described in Table 1 were

measured during the OCAT for overall task

completion time and for each addition sequence.

Addition sequences had varying cognitive

workloads (low, medium, and high in the form of

first, second, and third number, respectively). The

raw gaze data obtained during the OCAT were pre-

processed to extract eye movement-related features.

Pre-processing involved cleaning artifacts using

filtering techniques followed by applying fixation and

saccade classification algorithms based on dispersion

thresholds (Pradhan et al., 2018). From the processed

data streams, the features related to saccades and

fixations, along with blinks and pupillary dynamics,

were computed to characterize participants’ cognitive

and oculomotor performance (Table 1). The features

listed in Table 1 were computed for each OCAT test

performed by every participant. The features

exhibiting significant deviation from a normal

distribution (skewness outside the range of -1 to 1)

were subjected to logarithmic transformation during

the data preprocessing stage. This transformation

aimed to reduce skewness and stabilize variance,

thereby improving the performance of the

classification model and predictive accuracy. It is

worth noting that, in addition to the 31 features listed

in Table 1, the age of the participants was also

included as an additional feature in the predictive

modelling.

To develop and evaluate the performance of the

machine learning models to predict the possible mild

cognitive impairment, the OCAT dataset was

randomly split into training and testing sets, with 80%

of the data used for training and the remaining 20%

reserved for testing. This stratified split ensured that

the class distribution between CN and PMCI was

preserved across both subsets, as summarized in

Table 2. To address the issue of class imbalance in the

training data, the Synthetic Minority Over-sampling

Technique (SMOTE) was applied. SMOTE generates

synthetic samples for the minority class (in this case,

PMCI class) by interpolating between existing

minority class instances, thereby improving the

model’s ability to learn discriminative patterns and

reducing bias toward the majority class. Table 3

shows the class distribution of training and testing

data after applying SMOTE.

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Table 1: Description of OCAT features categorized into

time and eye-movement related features.

Models were developed and evaluated using both

the original imbalanced dataset and a class-balanced

dataset generated through SMOTE. The training data

were standardized using the StandardScaler method

to ensure that features had zero mean and unit

variance. The fitted scaler parameters were saved and

subsequently applied to the testing dataset to maintain

consistency in feature scaling during model

evaluation.

Table 2: Class distribution of training and testing dataset

from the original, imbalanced dataset.

Classification CN Class PMCI Class

Trainin

dataset 135 29

Testing dataset 31 11

Table 3: Class distribution of training and testing dataset

after applying SMOTE to get an augmented, class-balanced

dataset.

Classification CN Class PMCI Class

Trainin

dataset 135 135

Testin

dataset 31 11

2.4 Feature Selection

Feature selection was conducted in a two-step process

to reduce redundancy and retain features with high

predictive value. First, highly correlated feature

groups were identified using Pearson’s correlation

coefficient, with a threshold of 0.8, consistent with

established practices for multicollinearity reduction

(Dormann et al., 2013). Each group consisted of two

or more features that exhibited mutual correlation

above this threshold. Within each group, a Random

Forest classifier was employed to assess the relative

importance of features, and only the feature with the

highest predictive importance was retained while the

others were discarded. In the second step, a univariate

evaluation of the remaining, uncorrelated features

was performed using a Decision Tree classifier. Each

feature was individually assessed based on its

Receiver Operating Characteristic - Area Under the

Curve (ROC-AUC) score. Features with ROC-AUC

scores less than or equal to 0.5 were excluded, as they

contributed no better than random performance in

classification.

Both models were trained using the pre-processed

and feature-selected dataset. Key performance

metrics including recall, precision, specificity, F1-

score, accuracy, and the area under the precision-

recall curve (AUPRC) were computed for

comprehensive assessment of predictive

performance. Figure 2 illustrates the complete

workflow of the model comprising feature extraction,

data pre-processing, feature selection, and predictive

modelling steps.

Time-related Features

1. Total Test Time (s)

2. Mean Time for Number 1 (low cognitive workload)

(s)

3. Mean Time for Number 2 (medium cognitive

workload) (s)

4. Mean Time for Number 3 (high cognitive

workload) (s)

5. (Log-) Mean Latency Time (ms)

(

)

Standard Deviation of Latenc

Time

(

)

Eye Movement-related Features

Fixations:

7. Mean Fixation Time for Number 1 (ms)

8. (Log-) Standard Deviation of Fixation Time for

Number 1 (ms)

9. Mean Fixation Size for Number 1 (mm)

10. (Log-) Standard Deviation of Fixation Size for

Number 1 (mm)

11. Mean Fixation Area for Number 1 (mm2)

12. (Log-) Standard Deviation of Fixation Area for

Number 1 (mm2)

13. Mean Fixation Time for Number 2 (ms)

14. (Log-) Standard Deviation of Fixation Time for

Number 2 (ms)

15. Mean Fixation Size for Number 2 (mm)

16. (Log-) Standard Deviation of Fixation Size for

Number 2 (mm)

17. Mean Fixation Area for Number 2 (mm2)

18. (Log-) Standard Deviation of Fixation Area for

Number 2 (mm2)

19. Mean Fixation Time for Number 3 (ms)

20. (Log-) Standard Deviation of Fixation Time for

Number 3 (ms)

21. Mean Fixation Size for Number 3 (mm)

22. (Log-) Standard Deviation of Fixation Size for

Number 3 (mm)

23. Mean Fixation Area for Number 3 (mm2)

24. (Log-) Standard Deviation of Fixation Area for

Number 3 (mm2)

Saccades:

25. Median Diagonal Saccadic Velocity (deg/s)

26. Median Horizontal Saccadic Velocity (deg/s)

27. Median Vertical Saccadic Velocity (deg/s)

Blinks:

28. Blink Rate (number of blinks per minute)

29. Median Blink Duration (ms)

Pupillary Dynamics:

30. Standard Deviation of Pupil Size (mm)

31. Coefficient of Variation of Pupil Size

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460

Figure 2: Workflow of the predictive modelling process.

3 RESULTS

By using OCAT testing, this study aimed to predict

whether a participant would score below 140 on the

DRS, reflecting a higher risk for the possibility of

MCI. The classification performance of Logistic

Regression (LR) and K-Nearest Neighbours (KNN)

was evaluated under both imbalanced (original) and

class-balanced (SMOTE- augmented) datasets with

different feature combinations described in Table 1.

Table 4 shows that the LR model trained on

SMOTE-balanced data containing both time-related

and eye movement-related features demonstrated

superior and consistent performance across all

metrics, achieving perfect precision (1.00),

specificity (1.00), and F1-score (0.95), with high

recall (0.91) and accuracy (0.97), indicating strong

discriminatory ability without overfitting. In contrast,

the LR model trained on the original imbalanced data

showed reduced recall (0.73 at DT = 0.45 and 0.64 at

DT = 0.5), while maintaining high specificity (0.97)

and precision (≥0.88), suggesting a bias toward the

majority cognitive normal class.

KNN also benefited from SMOTE, with the

balanced model (k = 6) yielding improved recall

(0.82) and F1-score (0.82), along with high

specificity (0.94) and AUPRC (0.95). Interestingly,

the KNN model (k = 5) with original, imbalanced

dataset achieved perfect precision (1.00) and

specificity (1.00), but at the cost of lower recall

(0.73), indicating under-identification of the minority

possibly MCI class despite overall high accuracy

(0.93). Both models show optimal performance under

the SMOTE-balanced condition, as also seen in the

precision-recall curve (Figure 3).

4 DISCUSSIONS

Multiple predictive models were developed using

time- and eye movement-based OCAT features. The

LR models trained on SMOTE-balanced datasets

consistently demonstrated the most balanced and

robust performance across all metrics. These results

highlight the utility of SMOTE in improving recall

while maintaining favorable balance between

precision and specificity. The model developed

yielded high recall, precision, specificity, F1-score,

accuracy, and AUPRC, indicating reliable predictive

power for diagnosing PMCI. This means that the

model developed is robust to false positives and

negatives. In the context of MCI screening, false

positives may lead to unnecessary patient expense

and stress, as well as provider time, but the screener

result does not definitively make the diagnosis.

Rather, it rather prompts further evaluation via DRS

or comprehensive neuropsychological assessment.

False negatives, on the other hand, pose a more

Towards Early Detection of Mild Cognitive Impairment: Predictive Analytics Using the Oculo-Cognitive Addition Test (OCAT)

461

Figure 3: Precision-Recall Curve of Logistic Regression (LR) and K-Nearest Neighbors (KNN) models using both SMOTE-

augmented balanced and original, imbalanced datasets with time and eye-movement features during OCAT.

Table 4: LR and KNN model performance with SMOTE-balanced and imbalanced datasets.

Model

Hyper-

parameter

Recall Precision Specificity F1-score Accuracy AUPRC

LR – SMOTE

DT = 0.45

DT = 0.5

0.91

0.95

0.97

0.95

LR - original

DT = 0.45

DT = 0.5

0.73

0.64

0.89

0.88

0.97

0.8

0.74

0.9

0.88

0.91

KNN- SMOTE Best k = 6 0.82 0.82 0.94 0.82 0.9 0.95

KNN - original Best k = 5 0.73 1 1 0.84 0.93 0.95

serious concern in cognitive screening, since missing

the indicators of MCI when it is present could delay

timely intervention. This is particularly critical now

that there are disease modifying treatments for

Alzheimer’s disease as well as for individuals with

other causes of cognitive impairment, such as

concussions, hypoxia, vascular or metabolic

conditions (Biessels & Whitmer, 2020; Pradhan et al.,

2019)

In such cases, early identification and intervention

may facilitate cognitive recovery, or prompt

additional diagnostic measures, including

neuroimaging and further cognitive evaluation

(Biessels & Whitmer, 2020). These findings support

the continued use of OCAT with integrated eye-

tracking in clinical settings, as garnering the broader

range of cognitively relevant features improves

diagnostic accuracy and enhances the tool’s potential

for early detection of cognitive impairment. There is

broad consensus that the total health care cost could

be greatly reduced by more efficient resource

utilization as well as earlier diagnosis and

intervention for cognitive and neurological disorders,

especially when traditional clinical diagnostic tools

lack sufficient sensitivity (Laguna et al., 2011;

Mosimann et al., 2005). As a screening tool, OCAT

can help to narrow the focus of resource deployment

to those at greater risk for disease and may be

applicable to other conditions that also affect eye

movement. For example, sports-related concussion

has also been associated with oculomotor dysfunction

such as saccadic eye movements, accommodation,

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462

smooth pursuit (tracking), fixation, and sensitivity to

light in 90% of athletes (Ciuffreda et al., 2007).

Further refinement of LR and KNN models that use

both eye-movement and time-based features could

lead to a highly sensitive screening tool in clinical and

sideline settings where subclinical markers of

cognitive impairment can inform diagnoses.

5 CONCLUSIONS

OCAT leverages the well-established relationship

between eye movement dynamics and cognitive

function to facilitate early detection of cognitive

decline associated with MCI or other neurological

disorders, like Alzheimer’s Disease, dementias,

traumatic brain injuries, substance use, and fatigue.

By combining reflexive saccadic eye movements

with time-based and attentional effects under varying

cognitive loads, OCAT provides a multidimensional

profile of cognitive performance. As a rapid, non-

invasive assessment tool, OCAT can be seamlessly

integrated into outpatient clinics, primary care

settings, and neurology practices. Its use as an initial

screening tool may assist clinicians in identifying

patients who would benefit from more extensive

evaluations, such as full-extent DRS testing,

ultimately conserving time, reducing medical and

insurance burdens. With further refinement and

dissemination, OCAT could serve as a standardized

intake instrument for both preliminary assessment

and longitudinal tracking of cognitive state. By

featuring OCAT into routine assessments, healthcare

providers can enhance early detection, streamline

cognitive evaluations, and improve patient outcomes

to reduce healthcare costs.

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