JMIR MEDICAL INFORMATICS

Nam et al

Original Paper

Explanatory Model of Dry Eye Disease Using Health and Nutrition
Examinations: Machine Learning and Network-Based Factor
Analysis From a National Survey
Sang Min Nam1*, MD, PhD; Thomas A Peterson2*, PhD; Atul J Butte2, MD, PhD; Kyoung Yul Seo3, MD, PhD; Hyun
Wook Han4, MD, PhD
1

Department of Ophthalmology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea

2

Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States

3

Department of Ophthalmology, Institute of Vision Research, Eye and Ear Hospital, Severance Hospital, Yonsei University College of Medicine, Seoul,
Republic of Korea
4

Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea*

these authors contributed equally

Corresponding Author:
Hyun Wook Han, MD, PhD
Department of Biomedical Informatics, CHA University School of Medicine, CHA University
335 Pangyo-ro
Seongnam, 13488
Republic of Korea
Phone: 82 318817109
Email: hwhan@chamc.co.kr

Abstract
Background: Dry eye disease (DED) is a complex disease of the ocular surface, and its associated factors are important forunderstanding and effectively treating DED.
Objective: This study aimed to provide an integrative and personalized model of DED by making an explanatory model of
DED using as many factors as possible from the Korea National Health and Nutrition Examination Survey (KNHANES) data.
Methods: Using KNHANES data for 2012 (4391 sample cases), a point-based scoring system was created for ranking factors
associated with DED and assessing patient-specific DED risk. First, decision trees and lasso were used to classify continuous
factors and to select important factors, respectively. Next, a survey-weighted multiple logistic regression was trained using thesefactors, and points were assigned using the regression coefficients. Finally, network graphs of partial correlations between factors
were utilized to study the interrelatedness of DED-associated factors.
Results: The point-based model achieved an area under the curve of 0.70 (95% CI 0.61-0.78), and 13 of 78 factors consideredwere chosen. Important factors included sex (+9 points for women), corneal refractive surgery (+9 points), current depression(+7 points), cataract surgery (+7 points), stress (+6 points), age (54-66 years; +4 points), rhinitis (+4 points), lipid-lowering
medication (+4 points), and intake of omega-3 (0.43%-0.65% kcal/day; −4 points). Among these, the age group 54 to 66 years
had high centrality in the network, whereas omega-3 had low centrality.
Conclusions: Integrative understanding of DED was possible using the machine learning–based model and network-based
factor analysis. This method for finding important risk factors and identifying patient-specific risk could be applied to othermultifactorial diseases.
(JMIR Med Inform 2020;8(2):e16153) doi: 10.2196/16153
KEYWORDS
dry eye disease; epidemiology; machine learning; systems analysis; patient-specific modeling

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Introduction
Background and Related Studies
Dry eye disease (DED) is defined as “a multifactorial diseaseof the ocular surface characterized by a loss of homeostasis of
the tear film, and accompanied by ocular symptoms” [1]. Dueto its multifactorial etiology, DED cannot be characterized bya single process and its management is complicated, in whichfinding the major causative factors behind DED is critical toappropriate treatment [1]. Therefore, identification of
DED-related factors may enable advances in diagnosis,
elucidative pathophysiology, therapy, and public education, as
well as improvement of general and ocular health [2]. Indeed,various nonmodifiable, modifiable, environmental, and medical
factors related to DED have been reported by observational
studies and population-based, cross-sectional epidemiologicalstudies [2]. DED risk factors are categorized as consistent,
probable, and inconclusive; age, sex, Meibomian gland
dysfunction (MGD), connective tissue disease, Sjogren
syndrome, androgen deficiency, computer use, contact lens
wear, estrogen replacement therapy, and medication use (eg,antihistamines, antidepressants, and anxiolytics) are identified
as consistent risk factors [2].
Previously, a limited number of DED-associated factors were
investigated using the Korea National Health and Nutrition
Examination Survey (KNHANES) [3]. Although KNHANES
consists of a large number of variables from health interview
questionnaires, health examinations, and nutrition surveys, they
were not fully utilized [3]. In addition, previous studies on DED
have identified DED-related factors, instead of building a DED
model to assess the risk of DED for new individuals [3-7].

Highlights of This StudyIn this study, we generated a point-based model with
DED-associated factors from KNHANES using machinelearning algorithms and Lasso regularization. These methods
can improve the model performance to predict DED by selecting
features from a large number of variables from a large dataset
without overfitting while preserving complex interactions among
features [8]. Furthermore, interactions among the factors were
explored by network analysis. When the network analysis wasapplied to the model, a systemic understanding of DED, whichcannot be achieved by conventional methods, was possible byshowing the linkages between the relevant factors. To the best

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Nam et alof our knowledge, this was the first attempt at building amachine learning–based model to evaluate the individual risks
of DED and visualize the state using the network graph of
DED-associated factors.

Methods
Overview of Survey Data
The design, methods, and data resource profile of KNHANES
are available on the Web and in publications [9-11]. In short,
KNHANES is an annual survey performed by the Korea Centers
for Disease Control and Prevention (KCDC) in the Republic ofKorea, which assesses the health and nutritional status of the
population [10]. KNHANES is a nationwide cross-sectional
survey of a representative set of 10,000 noninstitutionalized
civilian individuals who are aged 1 year and older. Both DED
assessment and food frequency surveys were conducted only
in 2012. In the 2012 KNHANES, 192 primary sampling units(PSUs) were drawn from about 200,000 geographically defined
PSUs nationwide; 20 final target households were sampled foreach PSU as secondary sampling units [9]. KNHANES V (2012)
was approved by the KCDC Research Ethics Committee(2012-01EXP-01-2C), and written informed consent was
obtained from all subjects.

Variable Inclusion
Four data files, HN12_ALL (health examination, health survey,
and nutrient survey), HN12_ENT (ear, nose, and throat
examination), HN12_EYE (eye examination), and HN12_FFQ(food frequency survey), were combined. DED was considered
to be present when a subject had been diagnosed with DED by
an ophthalmologist (the variable E_DES_dg) and wasexperiencing dryness (E_DES_ds). Conversely, patients were
defined as DED-negative in the absence of both a diagnosis andsymptoms. E_DES_dg and E_DES_ds are available for persons
who are aged 19 years and older [11].
The included variables are listed in Textbox 1, and the overall
analysis is summarized in Figure 1.
All variables were available for subjects aged 19 years and older
except those of food frequency (19-64 years) and osteoarthritis
radiology (≥50 years) [9]. The LDL level was calculated using
the Friedewald equation, LDL=total cholesterol−(HDL+TG/5),
with exclusion of TG levels of higher than 400 mg/dL [12].

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Textbox 1. Included study variables of the Korea National Health and Nutrition Examination Survey data (2012).
Health examination data
Physical examination•

Body mass index (BMI), rhinitis, sinusitis, blepharoptosis [11], and cataract

Blood test results•

Anemia, hemoglobin, hematocrit, iron, total iron-binding capacity, ferritin, hemoglobin A1c, white blood cell count, platelet count, red blood
cell count, aspartate aminotransferase, alanine aminotransferase, creatinine, urea nitrogen, and vitamin D

Fasting (≥8 hours) blood parameters•

Sugar level, low-density lipoprotein cholesterol (LDL) level, high-density lipoprotein cholesterol (HDL) level, triglyceride (TG) level

Hypercholesterolemia definition: total cholesterol (TC)≥240 mg/dL or lipid-lowering medication
Hypertension definition: systolic blood pressure≥140 mm Hg, or diastolic blood pressure ≥ 90 mm Hg, or medication
Diabetes mellitus definition: fasting blood sugar level≥126 mg/dL, or diagnosis, or medication, or insulin injection
Fundus photography•

age-related macular degeneration, diabetic retinopathy

Osteoarthritis on radiology
Health survey data•

Age, educational stage, occupational class, household income, weight changes in the last year, mean duration of sleep per day, stress recognition,
current smoker, frequency of drinking alcohol, activity level, lipid-lowering medications, diagnosed glaucoma, eye surgery, and menstruation

Diagnosed current diseases•

dyslipidemia, depression, stroke, myocardial infarction or angina, rheumatoid arthritis, thyroid disease, atopic dermatitis, and asthma

Diagnosed cancers•

stomach, colon, breast, cervix, and thyroid

Food frequency survey data (daily intake)•

Energy, carbohydrate, protein, total fat, n-3 polyunsaturated fatty acid, n-6 polyunsaturated fatty acid, saturated fatty acid, cholesterol, fiber,
vitamin A, vitamin B1, vitamin B2, vitamin C, niacin, iron, calcium, potassium, phosphorus, and sodium

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Figure 1. Flowchart of overall analysis steps. KNHANES: Korea National Health and Nutrition Examination Survey.

Subsampling of Training and Test Sets andCategorization of Factors
Most DED cases (80.00%, 3513/4391) were used as training,
and the other cases were used for testing. Likewise, non-DED
cases were subsampled into training and test sets in the sameway. Next, the categorization or recategorization of the factors
was performed for the training set in consideration of reference
values. Here, optimal cutoffs were determined by training adecision tree on the training data and using binarized decision
tree rules as factors in the final regression model [13,14].
Missing values of each variable were classified as a separate
class.

Factor Selection Using Lasso (Least Absolute
Shrinkage and Selection Operator)
Factors were transformed into dummy-coded variables, in which
the largest category was used as reference and was excluded
during model construction, and missing values were not includedin the Lasso procedure.
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Lasso trained using cross validation was applied to thetransformed dummy variables with area under the curve (AUC)
as a stopping metric and wt_tot as the sample weight for the
analysis of the associations between the health interview, healthexamination, and nutrition survey. To regularize the model, we
selected the optimal lambda using cross validation (lambda.1sein glmnet, ie, the lambda that yields an error one standard error
away from the minimum error).

Construction of a Model for Dry Eye Disease
Using the lasso-selected factors, a survey-weighted multiple
logistic regression model was constructed from the complex
survey design of KNHANES. The survey design was represented
using the variable psu for PSU and ID_fam for the secondary
sampling unit, kstrata for strata, and wt_tot for weights.

Developing a Point-Based Scoring System for Dry Eye
DiseaseA point-based scoring system was developed by multiplying
the coefficients of factors in the survey-weighted regression
model by 10 and rounding to the nearest integer [15]. The total
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score of each individual in the training set was determined bysumming the points for factors accurately describing that
individual. Next, performance was assessed using weighted
receiver-operating characteristic (ROC) curves and the AUCs
with survey sample weight (wt_tot). An optimal cutoff for the
point-based system was determined by maximizing Youden’s
index value (sensitivity+specificity−1).

Testing the Point-Based Scoring System for Dry Eye
DiseaseThe model’s performance was assessed using the test set. The
AUC’s confidence interval was calculated; sensitivity andspecificity were reported using the point-based system’s cutoff
determined from the training set.

Analysis of Dry Eye Disease-Risk Factors
A survey-weighted multiple logistic regression analysis was
performed using the factors selected by lasso. Odds ratios (ORs)were calculated by exponentiating the coefficient derived bylogistic regression. Estimated population counts and proportions
for categories were computed.

Network Analysis of Dry Eye Disease-AssociatedFactors
With the training set, a correlation matrix for the
DED-associated factors was created. Weighted Pearson
correlation coefficients between two variables were calculated.
Next, a network graph was plotted by setting the graph argument
to “glasso” and the layout to “spring.” A partial correlation
network was drawn using the graphical lasso algorithm and theExtended Bayesian Information Criterion by which false positiveedges were controlled. Each edge represents the relationshipbetween 2 nodes after controlling for all other relationships in
the network [16,17]. The Fruchterman-Reingold algorithm isapplied with the “spring” layout, in which the lengths of edges
are dependent on their absolute weights [16]. Green edges
indicate positive weights (correlations) and red edges indicate
negative weights. Color saturation and edge width correspondto the absolute weight relative to the strongest weight in thegraph. Node size was proportional to the z-score for the absolute

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Nam et alpoint of the factor. Nodes were grouped as significant (P<.05,
risk factor analysis) or possible (P≥.05, risk factor analysis).
Three centrality indices (strength, closeness, and betweenness)were computed. Centrality is the absolute sum of the edge
weights connected to the node, closeness is the sum of theshortest distances from the node to all other nodes in the
network, and betweenness is the number of times in which thenode lies on the shortest path between 2 other nodes [17,18].

Statistics and Software
R version 3.6.1 and variable functions from its packages wereused: decision tree, “rpart” in the caret package (using
down-sampling and cross-validation) [19,20]; dummy-codedvariables, “dummy.code” in the psych package [21];cross-validation for Lasso, “cv.glmnet” in the glmnet package[22]; survey-weighted multiple logistic regression, “svyglm”
in the survey package [23]; weighted ROC curve and AUC,
“WeightedROC” and “WeightedAUC,” respectively, in the
WeightedROC package [24]; confidence interval of AUC,
“withReplicates” in the survey package [23]; estimation of
population counts and proportions, the survey package [23];general graphs, the ggplot2 package [25]; weighted correlation,“wtd.cor” in the weights package [26]; network graph, “qgraph”
in the qgraph package [16]; and centrality indices, “qgraph”
and “centralityTable” in the qgraph package [16].

Results
Point-Based Scoring Model for Dry Eye Disease
Total sample sizes for DED and non-DED were 575 and 3816
cases, respectively. The estimated prevalence of DED was
10.5% (SE 1.0%): 5.3% (SE 1.0%) for men and 15.9% forwomen (SE 1.0%). A total of 13 factors were selected by lasso
and the point-based scoring system for each factor is outlinedin Table 1.
Using this scoring system on the test set achieved an AUC of
0.70 (95% CI 0.61-0.78; Figure 2). Sensitivity and the specificity
were 0.66 and 0.68, respectively, at a cutoff of 10 points.

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Table 1. Point-based scoring system for assessing individual risk of dry eye disease using coefficients from a survey-weighted multiple logistic regression
model.
Variables

Regression coefficient (beta)

Standard error

Pointsa

Women

.865

0.182

9

Corneal refractive surgery

.903

0.281

9

Current depression

.709

0.294

7

Eye surgery: cataract

.705

0.196

7

Perceived stress: much to extreme

.560

0.136

6

Other ocular surgeries

.646

0.258

6

Phosphorus intake <746 mg/day

.454

0.182

5

Age 54-66 years

.396

0.179

4

Rhinitis by physical examination

.384

0.144

4

Lipid-lowering medications

.408

0.194

4

Cholesterol intake ≥240 mg/day

−.145

0.151

−1

Current smoker

−.441

0.242

−4

Omega-3 intake, 0.43%-0.65% kcal/day

−.407

0.172

−4

a

Calculated by multiplying the coefficient of the variable by 10 and rounding to the nearest integer. A positive point means a positive predictor for dry
eye disease. Dry eye disease is indicated when the sum of all points is 10 or higher.
Figure 2. Weighted receiver-operating characteristic (ROC) of the point-based scoring system for predicting dry eye disease. ROCs for train and test
sets were compared. AUC: area under the curve.

Risk Factor Analysis For Dry Eye Disease

point-based model were women, corneal refractive surgery, andcurrent depression (Tables 1 and 2). Omega-3 intake between

In the risk factor analysis, 10 of the 13 variables were significant(P<.05; Table 2). The top 3 significant risk factors in the
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0.43% (1003 mg for total 2100 kcal) and 0.65% (1517 mg for
total 2100 kcal) was a significant protective factor.
Population counts (n), proportions (%), and ORs were estimatedaccording to complex survey design. ORs and P values were

calculated by multiple logistic regression including all listed
variables. The missing data category for each variable were
included for calculation but not shown in the table.

Table 2. Population counts (n), proportions (%), and odds ratios of variables in the points-based scoring system for dry eye disease.
Healthy, n (%)

Dry eye disease, n (%)

ORa (95% CI)

P valueMen

16,277,579 (54.19)

911,587 (25.90)

Reference

Reference

Women

13,761,300 (45.81)

2,607,754 (74.10)

2.6 (1.8-3.6)<.001

None to a little

21,872,165 (72.81)

2,177,913 (61.88)

Reference

Reference

Much to extreme

6,864,096 (22.85)

1,202,940 (34.18)

1.6 (1.2-2.0)

<.001

None

26,673,187 (88.80)

2,701,904 (76.77)

Reference

Reference

Cataract

1,185,785 (3.95)

276,588 (7.86)

1.8 (1.2-2.6).004

Corneal refractive

1,150,662 (3.83)

339,857 (9.66)

2.8 (1.7-4.6)

<.001

Other

1,029,243 (3.43)

200,991 (5.71)

1.6 (1.0-2.5)

.08

No

21,295,695 (70.89)

2,180,596 (61.96)

Reference

ReferenceYes

8,047,298 (26.79)

1,243,634 (35.34)

1.5 (1.2-2.0)

.001

No

27,360,794 (91.08)

3,087,286 (87.72)

Reference

Reference

Yes

1,315,445 (4.38)

282,072 (8.01)

1.5 (1.1- 2.0)

.02

<54 or ≥66

25,121,235 (83.63)

2,757,338 (78.35)

Reference

Reference

54-66

4,917,644 (16.37)

762,003 (21.65)

1.4 (1.1-2.0)

.02

No

25,591,681 (85.20)

2,616,142 (74.34)

Reference

Reference

Yes

456,573 (1.52)

153,162 (4.35)

1.9 (1.1-3.3).02<0.43% or >0.65%

13,804,921 (45.96)

1,812,313 (51.50)

Reference

Reference

0.43%-0.65%

10,414,321 (34.67)

857,985 (24.38)

0.7 (0.5-1.0)

.04

≥746

19,948,841 (66.41)

1,940,991 (55.15)

Reference

Reference<746

4,270,401 (14.22)

729,307 (20.72)

1.4 (1.0-2.0)

.09

No

21,334,370 (71.02)

2,950,840 (83.85)

Reference

ReferenceYes

7,399,648 (24.63)

430,013 (12.22)

0.7 (0.5-1.1)

.14

<240

12,037,627 (40.07)

1,575,001 (44.75)

Reference

Reference≥240

12,181,614 (40.55)

1,095,296 (31.12)

0.9 (0.7-1.2).57

Factors
Sex/gender

Perceived stress

Eye surgery

Rhinitis on inspection

Lipid-lowering medications

Age (year)

Current depression

Omega-3 intake (kcal/day)Phosphorus intake (mg/day)Current smoker

Cholesterol intake (mg/day)a

OR: odds ratio.

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Network Analysis for Dry Eye Disease Model
In Figure 3, model factors are depicted in a partial correlation
network with centrality indices. The network-based factor
analysis in Figure 3 allows for the interrogation of theinterrelatedness of factors associated with DED, with larger
nodes representing factors’ importance (points), green nodes

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representing protective factors, and red nodes representing risk
factors. According to centrality indices (Figure 3), Age 54-66y(node 8) had high centrality in the network. For other ocularsurgeries (node 6) and omega-3 (node 13), the closeness indiceswere too low to calculate owing to lack of the connections toother nodes.

Figure 3. The partial correlation (adaptive LASSO) network (A) and the centrality indices (betweenness, closeness, and strength) (B) of the factors
associated with the dry eye disease. Factors were positively (risk) or negatively (protective) associated with dry eye disease. Factors can be significant(P<.05) or possible (P≥.05) according to the risk factor analysis. The node size is proportional to the absolute value of the point for the node’s variable.
Green and red edges mean positive and negative connections, respectively. The edge with the highest absolute weight will have full color saturation
and be the widest.

Four significant risk factors were linked in succession fromwomen (node 1) to current depression (node 3), much to extreme
stress (node 5), and rhinitis (node 9). Other serial connections
were found in three significant risk factors, age 54-66y,
lipid-lowering medication (node 10), and cataract surgery (node4). Nonsignificant factors were strongly connected with othersignificant factors, for example, current smoker (node 12) towomen, and cholesterol intake (node 11) to age 54-66y. Another
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nonsignificant factor, phosphorous intake (node 7), was closely
associated with the cholesterol intake.

Discussion
Principal Findings
Our model showed moderate performance for DED prediction
with a point-based scoring system in which the maximum AUC
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might reach 0.78. Our study chose a stricter definition of DED
because the individuals were not only required to be
symptomatic but also have a physician diagnosis. In addition,
the absence of DED was rigorously defined as a lack ofsymptoms and no physician diagnosis in the past. According to
the TFOS DEWS II report, DED is diagnosed on the basis of
the presence of a symptom and positivity for one or more
homeostatic markers [1]. Our DED definition more reflected adiagnosis of DED, and thus, the prevalence could be lower thanthat of prior studies that used a symptomatic definition [2].
However, even our definition was imperfect because diagnostic
tests were not performed and might be biased by the availabilityof a clinic in the local area or by the respondent’s condition.
This may explain, in part, the moderate diagnostic performanceof our DED model.

Reasoning for Machine Learning and Point-Based
Scoring Model
Machine learning algorithms and techniques were used forseveral purposes. First, tree-based machine learning was applied
to categorize continuous variables. Second, Lasso was
implemented to select important factors to simplify the modeland to reduce overfitting. Third, the models were generatedusing a training sample and validated with a separate test sample,
which enabled estimation of predictive power. This technique
is preferred because standard regression modeling and automatedvariable selection (eg, stepwise selection, pretesting of candidate
predictors) can result in overfitting [27,28]. As a result, ourmodel was robust enough to generalize to populations not usedduring training without overfitting (Figure 2).
Point-based scoring systems are useful for describing therelationship between multiple factors and the risk of thedevelopment of a disease [15]. Likewise, using our point-basedmodel, DED can be assessed by summing the points accurately
describing an individual with a cutoff of 10 points, indicatinghigh risk for DED. In addition, the node size was determinedby its point, and interrelatedness of DED risk factors wasinterrogated. Because DED was predicted by the sum of points,
larger nodes might be prioritized in evaluating DED.

Interpretation for Indirect Model Factors With
Network Analysis
By risk factor and network analyses, significant factors were
presumed to be directedly associated with DED, whereas
nonsignificant factors might be indirectly associated.
Conventionally, nonsignificant factors might have been
confounding variables that are related to DED via othersignificant factors. The network graph showed that
nonsignificant factors such as phosphorus <746 mg/day (node7), current smoker (node 12), and cholesterol ≥240 mg/day(node 11) were connected to significant factors such as women(node 1) and age 54-66y (node 8; Figure 3). However, thosenonsignificant factors were necessary to maximize the model
performance and selected by a machine learning–based Lasso
regression. Therefore, they seemed to be included to tune pointsof other significant factors without a causal effect on DED. For
example, current smoker (node 12) had a negative effect onnode 1 (women) because it generally occurred in men rather
than women. Smoking has been reported as an inconclusive
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Nam et alrisk factor for DED, and our study did not suggest smoking as
a risk factor [2].

Known Factors in Dry Eye Disease ModelIn the network-based analysis in Figure 3, age 54-66y (node 8)
showed high centrality in the network, which means that it has
more connections (strength), it is closer to other nodes(closeness), or makes connections between other nodes(betweenness). This high-centrality node exists at the center ofthe network and acts as hubs that connect disparate nodes [18].
In contrast, omega-3 intake (node 13) and other ocular surgeries(node 6) were independent nodes with low centrality.
In the previous study with KNHANES 2010-2011 by Ahn et al[3], 50- to 59-year-old and 60- to 69-year-old groups arepresented as risk factors, which are in agreement with our age
factor of 54 to 66 years. Other risk factors suggested (women,
extreme stress, cataract surgery, refractive surgery, other ocularsurgery) were also picked up in our model except for thyroid
disease and educational level [3]. Thyroid disease is a possible
risk factor, and the previous study argues an ambiguous link
between thyroid disease and DED [2,3]. The difference between
our work and the previous study can come from different
definitions of DED because we used both the diagnosis andsymptoms to classify an individual as having DED, whereas
the previous study used the criteria of having either the diagnosis
or symptoms [3].
Female sex is consistently associated with DED throughout thestudies, but the prevalence of DED is considerably variable inthese studies with respect to sex and age [2]. Stress has been
associated with DED as a trigger or an immune response
modulator [2,3]. Ocular surgery can cause DED in various ways,
for example, the exposure to strong light of the microscopeduring the surgery, use of anesthetic or postoperative eyedrops,
and the corneal nerve damage [3]. Specifically, refractive
surgery leads to neuropathic dry eye by sensory nerve damage,decreased tear secretion, and induced neurogenic inflammation[2].

New Factors in Dry Eye Disease Model
Depression, rhinitis, lipid-lowering medication, and omega-3
intake were new DED-associated factors in our model that wereadded to previously reported factors of KNHASES [3]. Those
factors have not been evaluated in the previous KNHASES
study on DED [3]. Depression (node 3) was positively connected
to node 1 (women), and a close association between depression
and DED in women has been reported [7]. Depression is moreprevalent in patients with DED partly because of somatizationand perceptional changes in ocular discomfort [29]. In addition,
depression was serially connected to other risk factors (Figure3), such as female sex, stress, and rhinitis, which may be utilized
for DED risk evaluation and control because positively
connected serial factors can occur together with possible
causalities. For rhinitis, allergic rhinitis was reported to besignificantly associated with DED, and inflammation is related
to both [30,31]. Notably, rhinitis was a clinically reliable factor
because it was diagnosed by physician’s examination.
Other serial risk factors were age 54-66y (node 8),
lipid-lowering medication (node 10), and cataract surgery (nodeJMIR Med Inform 2020 | vol. 8 | iss. 2 | e16153 | p. 9
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JMIR MEDICAL INFORMATICS
4). Dyslipidemia and its treatment might be an issue for the 54to 66-year-old group, which could explain the negative
connection between node 8 and node 11 (cholesterol >240
mg/day). Dyslipidemia has been suggested to induce MGD, amajor cause of DED [5,32]. However, oral statin therapy, nothypercholesterolemia, were recently reported to be associatedwith the symptoms of DED [33]. Interestingly, sterols havebeen reported to reduce cataract severity [34,35], and cholesterol
metabolism might be linked to cataract formation [36].
The results of randomized controlled trials for DED treatment
effect of omega-3 have been inconsistent, and larger studies
suggest no statistically significant improvement compared withplacebo [37,38]. Nonetheless, omega-3 has been commonlyused to treat DED in the clinic because essential fatty acids,
including omega-3, display anti-inflammatory properties [39],enhance the lipid layer of the tear film, and improve tearsecretion while lacking association with substantial side effects[2]. However, it remains a problem that there is no consensus
on the dose of supplementation, and our study suggested that1000 to 1500 mg daily intake of omega-3 (for 2100 kcal averagecalorie intake) helped to prevent DED. It was noteworthy that

Nam et alomega-3 intake might be used to treat DED without possible
effects on other factors because it did not have a connection in
the network (Figure 3).

Limitations
This study has several limitations. Eye-related factors(blepharitis, lid abnormalities, low blink rate, other ocularsurface disease, or conjunctivochalasis) and Sjögren syndrome
could not be assessed [40]. In addition, some nutrient factors
might have been missed because nutrient intake data wereavailable only for subjects younger than 65 years [9].

Conclusions
In summary, the machine learning–based model to assess theindividual risks of DED was successfully created from alarge-scale national survey data. With this model, additional
DED-associated factors could be suggested, and personalized
medical advice was possible using the network graph of themodel factors. These approaches allowed integrative
understanding of DED and may be applied to other multifactorialdiseases.

Acknowledgments
The authors would like to thank Jeremy D Keenan (Department of Ophthalmology, University of California San Francisco, San
Francisco, CA) who inspired the authors to incorporate network analysis to show medical relevance. This work was supportedby the National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2019R1C1C1007663 andNRF-2017R1E1A1A03070934).

Conflicts of Interest
None declared.

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Abbreviations
AUC: area under the curve
DED: dry eye disease
KCDC: Korea Centers for Disease Control and Prevention
KNHANES: Korea National Health and Nutrition Examination Survey
MGD: Meibomian gland dysfunction
NRF: National Research Foundation of Korea
OR: odds ratio
ROC: receiver-operating characteristic

Edited by G Eysenbach; submitted 06.09.19; peer-reviewed by DA Forero, J Bian; comments to author 29.10.19; revised version
received 23.11.19; accepted 16.12.19; published 20.02.20
Please cite as:
Nam SM, Peterson TA, Butte AJ, Seo KY, Han HW
Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor
Analysis From a National Survey
JMIR Med Inform 2020;8(2):e16153
URL: http://medinform.jmir.org/2020/2/e16153/
doi: 10.2196/16153
PMID: 32130150©Sang Min Nam, Thomas A Peterson, Atul J Butte, Kyoung Yul Seo, Hyun Wook Han. Originally published in JMIR Medical
Informatics (http://medinform.jmir.org), 20.02.2020. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright andlicense information must be included.

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