Predicting Comorbidities in Diabetic Patients and Visualizing Data

for Improved Healthcare

Giridhar Krishnan

and Waqar Haque

Department of Computer Science, University of Northern British Columbia, Prince George, Canada

Keywords: Predictive Modeling, Health Informatics, Diabetes, Comorbidities, Health Care Systems.

Abstract: Diabetes is one of the most common chronic diseases in the world with patients being more susceptible to

develop additional comorbidities over time. In this research, we have used clinical data collected over six

years to perform predictive and visual analytics which enables healthcare professionals gain valuable insight

into early identification of the risk of developing comorbidities thereby resulting in effective diabetes

management and reduced burden on healthcare system. We first present predictive models developed to

forecast the likelihood of one of the three common comorbidities for diabetic patients – Benign Hypertension,

Congestive Heart Failure, and Acute Renal Failure. The models use advanced data mining algorithms such as

Logistic Regression, Neural Network, CHAID, Bayesian Network, Random Forest and Ensemble. Results

from these models are incorporated into an interactive assessment tool that can take user input and predict the

likelihood of developing one of these comorbidities. In addition, an interactive diabetes dashboard presents

aggregated data using visually appealing charts, graphs, and tables. The dashboard also provides drilldown

capabilities to allow navigation at finer granularities of various metrics.

1 INTRODUCTION

Diabetes is a chronic disease in which the body

cannot either produce or utilize insulin. Type 1

diabetes (T1D) occurs when body does not produce

enough insulin; Type 2 diabetes (T2D) starts with

insulin resistance and can progress to a lack of

insulin; and gestational diabetes occurs in pregnant

women with no history of diabetes. According to the

Public Health Agency of Canada (PHAC), 5 to 10%

of diabetes patients have T1D and the remainder have

T2D; four percent of all pregnant women are affected

by gestational diabetes. Diabetes puts a great burden

on patients as well as the healthcare system as it can

also lead to comorbidities including stroke, heart

attack, kidney failure, blindness, and amputations. In

Canada, 3.9 million people have been diagnosed with

diabetes and more than one million have the disease

but are yet to be diagnosed. Statistics for prediabetes

are also a great concern with an alarming number of

5.7 million Canadians. Cumulatively, this number is

expected to reach 33% of the population by 2025

(Diabetes Canada).

https://orcid.org/0000-0003-0061-307X

https://orcid.org/0000-0002-6921-8097

Early detection of prediabetes and early diagnosis

of T2D can be accomplished using predictive models

which analyze patterns and correlations in historical

data. Data mining is vital in discovering hidden

patterns which could potentially improve quality of

life of patients diagnosed with health conditions. Data

mining algorithms are used to identify correlations

between different variables and build predictive

models which provide insightful information for the

purpose of clinical administration, diagnosis as well

as management of diabetes. When interfaced with

data warehousing, this can enable data-driven

decision making by facilitating complex analyses and

visualization through multi-dimensional Online

Analytical Processing (OLAP) cubes.

The work presented in this paper has two major

contributions. First, predictive models were

developed to find the likelihood of one or more of

three representative diabetes comorbidities - Benign

Hypertension, Congestive Heart Failure, and Acute

Renal Failure, using data mining algorithms. The

results from these models could be used by healthcare

professionals to identify patients who are at higher

Krishnan, G. and Haque, W.

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare.

DOI: 10.5220/0011628800003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 52-63

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

risk of developing predicted comorbidities and ensure

effective management of the disease or development

of other complications. The results are incorporated

into an interactive web form which predicts the

probability of one of the three comorbidities based on

the inputs provided. The second contribution is the

design and development of an interactive dashboard

for a better and deeper understanding of the metrics

associated with the disease. The dashboard provides

aggregated statistics at facility and community levels

with drill-down and drill-through reporting at finer

granularities for various demographics, health

regions and diagnoses. Overall, the extracted

information can be useful to identify the gaps in

healthcare and enhance related services by making

informed decisions in a timely manner.

2 RELATED WORK

The cost and toll of diabetes on global healthcare

systems has prompted extensive research especially

with focus on early diagnosis and detection of the

disease. The literature review presented in this section

is grouped in three categories. Firstly, representative

studies on diabetes and data mining are presented.

This is followed by a review of the research-based

tools such as diabetes calculators. Finally, the use of

data visualization for enhanced and cost-effective

healthcare is explored.

Automated detection of diabetes mellitus using

artificial neural networks (ANNs) without patients

undergoing clinical tests was proposed (Kumari &

Singh, 2013). The neural network was built using the

backpropagation algorithm and 18 out of 20 datasets

tested produced results with an overall accuracy of

92.8%. The variables used to build the model

included age, gender, weight, height, weight loss,

thirst, hunger, appetite, nausea, fatigue, vomiting,

bladder and skin infections. Considering that the data

was collected using surveys and was self-reported,

the authenticity of the diagnosis becomes

questionable. Another study was conducted to

compare three data mining models (ANN, decision

tree and logistic regression) to predict diabetes or

prediabetes by various risk factors (Meng, Huang,

Rao, Zhang, & Liu, 2013). A questionnaire to obtain

information on demographics, family diabetes

history, anthropometric measurements and lifestyle

risk factors was given to 1457 participants, 735 of

whom had diabetes. The input variables used in this

study included age, gender, family history of

diabetes, marital status, education level, work stress,

duration of sleep, physical activity, preference for

salty food, eating fish, drinking coffee, and body mass

index. The output variable (a flag) indicated whether

the person had diabetes/prediabetes or not. It was

observed that the decision tree yielded the highest

classification accuracy, followed by logistic

regression and ANN. A limitation of this study was

that the sample population chosen was only from two

small communities in Guangzhou, China and was not

representative of any larger population. In a recent

study, data was collected from over 230,000

participants over a ten-year period to develop a T2D

risk prediction model using machine learning

algorithms (Zhang, et al., 2020). This research

excluded all diabetic participants as well as any

participants taking medication for diabetes. The

collected medical, behavioral, demographic and

incidence data was used to predict T2D in participants

at 3, 5, 7 and 10 years using a longitudinal study.

Three machine learning algorithms (random forest,

multilayer feedforward artificial neural network, and

a gradient boosting machine approach) were

compared with conventional logistic regression

model. The AUC (Area under Curve) in machine

learning models was higher than the conventional

regression model implying better prediction

capabilities of the former. The highest accuracy was

recorded by gradient boosting algorithm with an AUC

of 79% in 3-year prediction and 75% in 10-year

prediction. It was also noted that diabetes incidence

was higher among men than women over the ten-year

period. Limitations of this research were that it used

self-reported data and the exclusion of participants

was done by use of diabetes related medication

instead of a clinical diagnosis.

Diabetic patients have higher risk of being

diagnosed with multiple comorbidities which, in turn,

increases the complexity of treatment and care.

Mortality of diabetic patients in ICU was predicted

using three metrics: Charlson Comorbidity Index

(CCI), Elixhauser Comorbidity Index and Diabetes

Complications Severity Index (DCSI) (Anand, et al.,

2018). The results showed AUC values to be 0.694,

0.682 and 0.656 for DCSI, Elixhauser and CCI,

respectively. The AUC improved to 0.785 when all

three metrics were combined using logistic

regression. A limitation of this research was that it

used random sampling of 70/30 for analysis which

resulted in an imbalance of less than 10% of positive

cases. Also, it did not consider patients directly

admitted for diabetes related care because it was

complicated to identify using recorded diagnostic

codes. The authors recommend analyzing other

variables, such as Length of Stay (LOS), and use of

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare

other machine learning algorithms such as random

forest and ANN for better predictions.

A tool to predict T2D was developed to identify

individuals at risk without undergoing laboratory

tests (Lindström & Tuomilehto, 2003). The risk

factors taken into account included age, BMI, waist

circumference, history of antihypertensive drug

treatment, high blood glucose, physical activity, and

daily consumption of fruits, berries, or vegetables.

For this study, a random population sample between

ages 35-64 was selected and followed for 10 years.

Each category was assigned a score using

multivariate logistic regression model coefficients.

The cumulative sum of all scores represented the

Diabetes Risk Score. The research identified 182

cases of diabetes incidence in 4,435 subjects. The

limitations of this study include exclusion of family

history from the risk factors, inclusion of individuals

with high glucose levels and use of surveys and

national population register for the data.

A simple tool for detecting undiagnosed diabetes

and prediabetes was proposed using survey data

(Heikes, Eddy, Arondekar, & Schlessinger, 2008).

The models were built using two methods –

classification tree analysis and logistic regression.

The risk factors used include age, height, waist

circumference, gestational diabetes, race/ethnicity,

hypertension, family history, and exercise. ROC area

under curve for undiagnosed diabetes was 0.85, and

for prediabetes was 0.75. ROC is used to evaluate the

performance of models where the true positive rate is

represented by sensitivity and false positive is

represented by specificity. This research eliminated

the variables for BMI in favour of height and weight,

and the cholesterol variables were eliminated due to

missing fields and low predictor value. Another

important variable eliminated was diabetes in any

blood relative.

Data visualization can lead to enhanced patient care

and optimized diabetes management. Research has

shown that management of diabetes improves when

patients are provided with information and

knowledge about their health condition. In a study,

results were integrated with a user-friendly tool to

predict the risk of hypertension, cardiovascular (Lau,

Campbell, Tang, J S Thompson, & Elliott, 2014).

Patients were assessed by a diabetologist and given

access to a web portal which had information

regarding diabetes, their personal health status as well

as the ability to contact the diabetologist. The primary

goal of this research was to monitor the blood glucose

levels (A1C) and to observe differences between

users who had access to the web portal and those who

did not. The study found that the web portal users had

lower levels of A1C compared to the non-users. This

study did not explore the demographic factors that

would influence the usage of the web portal and did

not distinguish between patients with T1D and T2D.

A clinical decision support system (CDSS) was built

for a project designed to explore predictive models

and decision support for T2D care and management

(Dagliati, et al., 2018). The dashboard consisted of

three sections consolidating metabolic control,

frequent temporal patterns and drug purchase

patterns. An outcome assessment and research

support system was designed for clinicians. It was

observed that T2D patients who had access to CDSS

recorded shorter durations with their clinical visits

and screening for complications increased in the visits

indicating optimized patient care. The dashboard was

evaluated for patient management but not for any

clinical outcomes.

In summary, while there has been focus on early

diagnosis and management of the disease, there is an

obvious research gap when identifying risk factors

leading to diabetes comorbidities. A common

limitation prevalent in existing work is the use of less

reliable survey data and interviews which could lead

to erroneous predictive models. In contrast, clinical

data is more authentic as patient diagnosis has been

confirmed by qualified physicians. However,

obtaining clinical data for research can be

challenging as it is seldom available in the public

domain due to privacy concerns.

3 METHODOLOGY

The dataset used in our research represents six years

of clinical data for diabetic patients diagnosed with

either T1D or T2D across a broad range of

communities and facilities. This research has three

interrelated components. First, a model for predicting

diabetes comorbidities is proposed. The model

includes the lesser explored variables such as length

of stay and access to family physicians. Second, the

results were integrated with a user-friendly tool to

predict the risk of hypertension, cardiovascular

disease, and renal failure in a specific diabetic patient.

Finally, an interactive dashboard has been developed

to provide insights about diabetes using visual

analytics which uncovers hidden data patterns and

assists in effective decision making and improved

healthcare outcomes.

HEALTHINF 2023 - 16th International Conference on Health Informatics

Figure 1: Components for Predictive Modeling and Data Visualization.

3.1 Proposed Model

The key components of the model are shown in

Figure 1. After importing the .csv data file into SQL

Server (Microsoft) database, the entire process can be

grouped into three distinct phases, namely, predictive

modeling, assessment tool and dashboard design. In

the first phase, data was cleansed and prepared for the

model before creating testing and training datasets for

the three comorbidities. Relationships were

established within the database to associate

demographic and diagnostic data for visualization.

Three predictor variables were chosen from the top

twenty diagnostic codes. A separate model was built

for each of these variables. The remaining diagnostic

codes together with demographic data then became

the input variables. Various data mining algorithms

such as logistic regression, decision tree and artificial

neural network together with their ensembles were

compared for relevance and accuracy. Secondly, the

results from predictive models were integrated with a

web-based, user-friendly assessment tool to predict

likelihood of comorbidities for individual patients.

The front end for the assessment tool consists of a

web form wherein the user enters information such as

age and diagnosis code. The tool then displays the

risk score for diabetes comorbidities based on the

selected backend predictive model. Finally, a visual

analytics dashboard was built to analyze/compare

various metrics together with drilldown capabilities

which allowed filtering by specific demographics and

at finer granularity.

3.1.1 Data Source, Pre-Processing &

Variable Selection

The clinical dataset used for this research represents

patients who had accessed one of the eighteen

facilities in three Health Service Delivery Areas

(hereinafter referred to as Areas A, B, and C). The

dataset consists of 141,900 records representing

34,824 unique admissions for the period 2012-2018;

there were no cases of gestational diabetes. The

dataset was anonymized to protect the privacy of

patients and International Classification of Disease

(ICD) codes have been used for the diseases.

To improve data quality, negative factors such as

missing values and inconsistencies were addressed

during pre-processing. For instance, pivot queries

were used to obtain unique admissions including

diagnosis codes for comorbidities. Three codes were

finally selected as predictor or target variables: 1)

I500 (Congestive Heart Failure), 2) I100 (Benign

Hypertension), and 3) N179 (Acute Renal Failure).

For each of these diagnosis codes, training and testing

datasets were initially created with a ratio of 70:30.

The final dataset combined patient’s multiple

admissions into a single record and retained all their

diagnosis codes to avoid data inconsistency. For

relevance, it was ensured that the data consisted of

only diabetic patients. Irrelevant and redundant codes

were eliminated using the Feature Selection (FS)

algorithm. The process was repeated for all three

target variables.

The ranking produced by the Feature Selection

algorithm for the three target variables is shown in

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare

Figure 2. It can be observed that the diagnosis codes

E1152, E1164, E119 and E149 were identified among

the top ten variables consistently for all three target

variables. This can be attributed to the large number

of patients diagnosed with these codes (Table 1).

Interestingly, I100 is also included as one of the top

three important variables for predicting I500 as well

as N179. Codes E119 and E149 consistently rank

below the top five variables. These two codes specify

diabetes patients without mention of complications

which indicates that the probability of these patients

to be diagnosed with other comorbidities is relatively

low. It was also observed that 75% of E119 patients

and 78% of E149 patients did not have either of I100,

I500 and N179. Similarly, 49% of E1152 and 39% of

E1164 patients were diagnosed with at least one or

more of the three comorbidities (target variables)

resulting in both codes to be ranked in the top five.

Only two percent of the patients included all three

target variables in their diagnosis. This is the reason

N179 or I500 does not rank as important variables

while predicting the others. Finally, the average age

of patients in the dataset was 63 years and the average

Total Length of Stay was seven days. Both of these

variables were ranked as important.

Table 1: Top Seven Diagnostic Codes.

Code Diagnosis Description Patients

E119 Type 2 diabetes mellitus

without complications

7,956

E1152 Type 2 diabetes mellitus with

certain circulatory

complications

3,763

I100 Benign hypertension 2,656

E149 Unspecified diabetes mellitus

without complication

2,105

E1164 Type 2 diabetes mellitus with

oor control, so describe

1,674

I500 Congestive heart failure 1,385

N179 Acute renal failure 1,303

3.1.2 Predictive Modeling

The data mining algorithms used for this study

include Artificial Neural Network (ANN), Logistic

Regression, CHAID, Random Forest, Bayesian

Network and Ensemble. These algorithms have

unique characteristics and are available in IBM SPSS

Modeler v18.1 (IBM, 2020) which also contains other

desirable features such as advanced statistical

analysis, ease of use, support for multiple data

sources, feature selection algorithm, scalability,

automation, visual interface and multiple deployment

methods. An integration package was built to connect

the Modeler with MS SQL server (Microsoft)

backend database.

Figure 2: Feature Selection Results (I100, I500, N179).

Figure 3 shows the training model for prediction

of I100 (hypertension) using five data mining

algorithms. The SQL Access data source node

establishes a connection to diabetes database and

extracts the dataset consisting of the finalized twenty-

six variables. Twenty-four of these variables were the

input variables and the remaining two variables were

excluded because they were either a unique identifier

(Patient Code) or the target variable (I100). The Type

node is used to specify the data type of the selected

variables as either nominal, categorical, continuous,

flag or ordinal. Additionally, this node allows to

specify whether a variable is input or target and

provides an option to specify a unique identifier

(Patient Code). The diagnosis codes, physician code

and the target variable were all assigned as a flag

datatype due to the binary values, such as 0 or 1.

Patient Code, Age, Average Length of Stay were

assigned as continuous which is used to describe

numeric values. Facility Health Service Delivery

Area and Facility Name were assigned as nominal

which is used for storing string values. The Type node

is connected to the data mining model nodes each of

which represent one of the five algorithms. Executing

these nodes generates the model nugget which

HEALTHINF 2023 - 16th International Conference on Health Informatics

contains the results of the trained model for the

selected algorithm. The results from the model nugget

are connected to the analysis node which calculates

the prediction accuracy of the model.

Figure 3: Predictive Model – Training.

Figure 4: Predictive Model – Testing.

Figure 4 shows the testing model used for predicting

one of the target variables (I100). In this figure, the

data sources represent the testing dataset which

includes 30% of patients with I100. A major

difference between the testing and training data

source / type is that the former does not contain

information of the corresponding target variable. The

trained model nuggets possess the required

information to predict the target variable using the

selected data mining algorithm. These results are

pushed to an output table for analysis.

Figure 5: Predictive Modeling Ensemble Training/Testing.

Figure 5 shows the training and testing models for the

Ensemble algorithm. The corresponding node

combines results from the five trained models and

generates a field containing the aggregated results

which are then passed to the analysis node. Note that

the Type node is connected to only one model nugget.

This is because the data types of the variables are

fetched from the first model nugget and then passed

to the other four model nuggets followed by the

Ensemble node. For testing, the five model nuggets

are connected to each other and then to the Ensemble

node which is connected to the Table node to generate

aggregated results for analysis.

The process described above is also implemented

for the other two target variables (I500, N179).

3.2 Model Accuracy

The results from the output table for all models were

pushed into the diabetes database. The predicted

column and the existing target variable information

was compared for each row and the statistical

accuracy of predictions was computed as the ratio of

accurate predictions and total values in the dataset. It

was observed that the accuracy of predictions for all

algorithms was consistently better for true negative

cases compared to true positives. This is due to the

comparatively fewer true positive cases in all

datasets.

Ensemble and Logistic Regression had the

highest accuracy for predicting patients with or

without hypertension (Figure 6). Both these

algorithms recorded identical accuracies of 84.15%.

The low accuracy (81.7%) of Random Forest can be

attributed to overfitting problem which is one of the

drawbacks of this algorithm. The dataset had 83%

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare

patients without hypertension and 17% patients who

were diagnosed with I100 hypertension. For patients

without hypertension, the six algorithms yielded an

average accuracy of 97.2%. However, the average

accuracy for those with hypertension was only 15%.

The reason for this low accuracy is the small number

of patients in this group for the training dataset.

Specifically, there were 2,656 patients with

hypertension which is only 18.9% of the total

patients.

Figure 6: Predictive Model Results for I100.

Figure 7 shows the accuracy of the trained models

for target variable I500 (Congestive Heart Failure).

Both Ensemble and Neural Network demonstrated

similar accuracy of 92.6%. Logistic Regression,

Bayesian Network and CHAID had accuracies of

92.5%, 92.5%, and 92.3%, respectively. The average

accuracy to predict patients with and without

congestive heart failure was 29.1% and 98.7%,

respectively. As explained earlier, the smaller number

of patients in the training dataset for this group (<10%

patients diagnosed with I500) contributed to the low

accuracy.

Figure 7: Predictive Model Results for I500.

Figure 8 shows the overall accuracy of all algorithms

for target variable N179 (Acute Renal Failure).

CHAID, Ensemble and Logistic Regression had

accuracies of 96.77%, 96.74%, and 96.55%,

respectively. Random Forest and Neural Network

recorded an identical accuracy of 96.37% and

Bayesian Network had an accuracy of 96%. Within

the database, there were a total of 1,303 patients who

were diagnosed with N179; these patients were split

into training and testing datasets in the ratio of 70:30.

The average accuracy for predicting patients with and

without N179 is 63.7% and 98.8%, respectively.

Figure 8: Predictive Model Results for N179.

In summary, all algorithms perform relatively similar

for each of the three target variables. This is likely due

to the fact that 1) Auto Classifier node was used to

identify the data mining algorithms with high

accuracies for all three target variables, 2) only the

important variables identified by FS algorithm were

selected as input variables, and 3) all twenty diagnosis

codes, including the target variable, had binary data

(0,1). Random Forest occasionally suffered from

overfitting problem that trained the models to learn

the noise thereby leading to negative impact on

accuracy.

An interesting observation was made when

predicting true positive cases only. For instance, for

N179, CHAID had the highest accuracy of 67.7%

followed by Ensemble with 66.4%. Bayesian

Network, Logistic Regression and Neural Network

had accuracies of 63.8%, 63.6% and 61%,

respectively. For reasons mentioned earlier, Random

Forest had the lowest accuracy (59.7%) for predicting

patients diagnosed with N179.

As the percentage of

diagnosed patients for a target variable decreased,

there was an increase in accuracy of predicting true

positives across all algorithms. Since N179 consisted

of the lowest percentage of diagnosed patients, it had

a comparatively higher accuracy for predicting true

positive cases, followed by I500 and I100.

Overall, the accuracy of predictions for all

algorithms was consistently better for true negative

cases compared to true positives.

4 COMORBIDITY ASSESSMENT

TOOL

To predict the likelihood of a specific comorbidity for

an individual case, an interactive web form has been

developed which uses the predictive models on the

backend. Specifically, the source node is replaced by

HEALTHINF 2023 - 16th International Conference on Health Informatics

a User Input node which allows the user to enter

values of all variables for a specific patient and

generates the corresponding output instantly. An

example for predicting I100 (hypertension) using this

tool is shown in Figure 9. The Ensemble algorithm

has been used in this case because it had the highest

accuracy for predicting I100 among all six

algorithms. The predicted value of 1 indicates that the

patient will have hypertension in future, and there is

a probability of 64% for this to happen. This patient

was diagnosed with multiple comorbidities, which

included the other two target variables (I500, N179).

The prediction was in conformance with the actual

data for this patient who was in fact diagnosed with

hypertension. The web form can be connected to any

of the six algorithms running in the background.

Figure 9: Comorbidity Assessment Tool.

5 DATA VISUALIZATION

Dashboards are an effective tool which allow

visualization of large amounts of data in an intuitive

manner without delving into complex statistics.

Interactive data visualizations can help users to

quickly identify patterns and trends that can enable

effective decision-making. The Canadian Diabetes

Association established that health professionals

were more effective in treating diabetes patients when

using a dashboard which provided knowledge of

other risk factors and associated guidelines (Diabetes

Canada. (n.d.).). Similarly, patients who are presented

with a dashboard listing the risk factors tend to benefit

from the knowledge contained therein. For this

research, Microsoft’s SQL Server Reporting Services

(SSRS) was selected to build the dashboard due to its

simplicity, capability to produce interactive

visualizations and ability to adjust with fast changing

datasets. Among other things, SSRS provides features

including compatibility with a variety of data sources,

interactive sorting capabilities, drill-down/drill-

through reporting, security via access controls, and

export features. Microsoft SQL server was used as the

backend database and a wrapper to render reports.

The dashboard consists of three top-level reports

and several drilldown sub-reports. The top-level

aggregated reports show overall aggregated statistics

for the dataset, prominent comorbidities and primary

diagnosis codes for patients with different types of

diabetes, and a comparative analysis of aggregated

patients and admission statistics across all health

service delivery areas. Figure 10 shows the main

dashboard that displays the clinical data sliced along

various dimensions such as population, diagnosis

codes, diabetes types, average age, admissions, and

comorbidities for patients admitted in respective

facilities over the years. The dashboard allows

navigation to reports at a finer granularity via

drilldowns. The dashboard presents insightful

information such as obesity rates based on

geographical locations, food habits and the overall

trend for diabetes over the years. Such information

can educate the users about diabetes and its impact on

health. The charts in the top row show an overview of

aggregated statistics. The admitted patients recorded

an average of four diagnoses from the possible 4,592

diagnosis codes. The maximum number of diagnosis

codes recorded for a patient was 89, there were four

patients who recorded more than 80 comorbidities

and fifty patients who recorded between 50-80

comorbidities. The geographic distribution of

diabetic patients and admissions is also shown.

Drilldown from this chart shows a further breakdown

of these numbers for each local health region. The

number of patients diagnosed with diabetes and

number of admissions show an interesting correlation

over the years. The T2D patient count consistently

dropped until it reached a plateau in 2017/18. On the

other hand, the admissions trend is quite the opposite

with 2017/18 recording the highest number of

admitted patients. Overall, residents in this province

had a lower prevalence of diabetes (5.7-5.9%) than

the national average of 6.5-7.3% over the study

period. A similar pattern was observed for T1D

patients.

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare

Figure 10: Diabetes Dashboard.

On average, a patient had four diagnosis codes

with the majority of patients having two to five

comorbidities. The lowest number of patients was

recorded for 16-20 comorbidities and then an increase

was observed for 20+ comorbidities. The next chart

shows the prominent communities that had the

highest number of diabetic patients. The dataset

consisted of 305 communities and seventy local

health regions of Community_1 recorded the

maximum number of patients consistently over the

years. The Hospital A in Community_1 accounted for

53% of the total patients and 47% of overall

admissions. Hospital B in Community_2 accounted

for 7% of the total patients and 13% of the overall

admissions. It was also observed that all communities

showed an increase of patients in the year 2017/18

from the previous year making it consistent with the

trends noted earlier. LHA_1, LHA_2, LHA_3 and

LHA_4 consist of 15, 3, 13 and 16 communities,

respectively. The last chart on this dashboard shows

prevalence of diabetes per thousand of the population.

LHA_1 recorded the maximum prevalence per

thousand residents over the study period. An

interesting observation is that while LHA_1 and

LHA_2 did not show any change between 2016/17

and 2017/18, both LHA_3 and LHA_4 showed a

slight increase over the same period. This is

consistent with the earlier observation where a spike

in the number of patients was observed for both LHAs

during this period.

5.1 Diabetes Types & Comorbidities

Figure 11 shows the overall aggregated statistics

broken down by diagnosis codes specific to the type

of diabetes and comorbidities. The clinical data has

been sliced along various patient groups (T1D, T2D

and other types of diabetes) and diagnosis. It was

interesting to note that 51% of the patients had been

diagnosed with between two to five comorbidities in

addition to diabetes.

The top chart shows vital statistics related to

comorbidities. It was observed that one in five

diabetic patients had hypertension and one in ten had

heart/renal failure. These three comorbidities

accounted for 39% of the total patients and 22% of

the total admissions. Further, comorbidities

accounted for 98% of the total diagnosis codes. Upon

admission, multiple diagnosis codes are normally

entered, one of which becomes the primary ‘most

responsible’ code. The top five primary diagnosis

codes which account for 23% of total patients and

HEALTHINF 2023 - 16th International Conference on Health Informatics

Figure 11: Diabetes Types/Comorbidities.

15% of total admissions are H251 (senile nuclear

cataract), H269 (unspecified cataract), I214 (acute

subendocardial myocardial infarction), I500

(congestive heart failure), and Z031 (suspected

malignant neoplasm). It is worth mentioning that

while H251 is showing the maximum number of

patients’ primary diagnosis, it is not the case when all

diagnosis types are included. For example, H251

accounted only for 4% of the total admissions and 7%

of the total patients. Thus, it was not identified as a

target variable when building the model. The top five

comorbidities for patients with T2D were benign

hypertension (I100), congestive heart failure (I500),

unspecified glomerular disorders (N0839), acute

renal failure (N179), and urinary tract infection

(N390). It was observed that 65% of the patients with

T2D were diagnosed with one or more of these

comorbidities, 48% were diagnosed with one or more

of the top three comorbidities (I100, I500, N179)

which were also selected as target variables for the

predictive model. The top five comorbidities

diagnosed for patients with T1D or any other types of

diabetes excluding T2D represented 95% of the total

patients in this group. The three target variables

accounted for 63% of patients. The top two

comorbidities (hypertension and congestive heart

failure) are the same in both sets (T2D and T1D);

however, the third and fourth comorbidities (N390

and N179) are reversed. Finally, the top five

diagnosis codes embedded with different types of

diabetes is shown. Four of these codes (starting with

‘E11’) represent T2D patients that can be attributed

to the fact that majority of the patients in this dataset

have been diagnosed with T2D.

Figure 12 shows a comparison of aggregated

statistics for each of the three HSDAs – A, B and C -

which recorded 24%, 57% and 19% of the total

patients, respectively. It was noted that 6% of the

patients migrated to other communities and were thus

counted more than once. This, however, does not

impact the number of visits because those are

recorded independent of the patient’s community. On

average, approximately two admissions per patient

were recorded across all HSDAs, including out-of-

province patients. Even though Region B recorded

majority of the patients as well as admissions, the

average length of stay (LOS) was very similar across

all HSDAs. A similar pattern was also observed for

patients who had family physicians. There was a total

of nineteen communities which recorded over 100

patients for the years 2012/13 to 2017/18. Among

these, Community_3 had 85% of patients without a

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare

Figure 12: Comparison across regions.

Figure 13: Patient Visits Comparison Report.

family doctor followed by other communities where

73%, 42%, 40%, and 33% of patients had no family

doctor. The five communities with the highest

number of patients had 14%, 27%, 18%, 14% and

11% patients with no family doctors. Region C had

the highest number of patients visiting from outside

of the province. A breakdown of out-of-province

visits is also provided at the facility level.

The number of patients who were recorded with

only one diagnosis code was less than 2% in each of

the HSDAs. Patients with two to five comorbidities

represented 53%, 50% and 59% of the total number

of patients in Regions A, B and C, respectively.

Patients with six or more comorbidities were 45%,

49% and 39% for the same HSDAs, respectively. The

three HSDAs had a variation ranging from 72% (C)

to 90% (B) for number of patients with diagnosis code

related to T2D. Of the eighteen facilities across all

HSDAs, Hospital_A admitted 50% of the total

patients followed by Hospital_C (11%) and

Hospital_D (10%). The lowest number of patients

was admitted by Hospital_E (0.5%). Figure 13 shows

the drilldown for annual breakdown of cumulative

visits and number of patients across all HSDAs.

HEALTHINF 2023 - 16th International Conference on Health Informatics

6 CONCLUSION

Diabetes is a chronic disease whose prevalence is

growing at a rapid rate throughout the world. In

Canada, one person is diagnosed with diabetes every

three minutes, and one in ten deaths are attributed to

this disease. Due to this prevalence, it has received

global attention and vast amounts of data has been

collected. Unfortunately, this data exists in disparate

repositories and has not been harnessed to its full

potential. One of the key shortcomings of existing

research towards this cause is the use of non-clinical

data which is collected using surveys and self-

administered questionnaires. The dataset used for this

research was obtained from a health authority and

exclusively comprised of diabetic patients. In order to

make this clinical data valuable for physicians and

other stakeholders, several KPIs were identified

which provided insight into historical trends and

patterns for using visual analytics. These metrics are

then presented on a visually appealing dashboard

which consists of top-level reports and numerous

drill-down and drill-through reports for insights at

finer granularity. The data was mined for predictive

analysis. Six representative data mining algorithms

were evaluated for analysis of three target variables.

Overall, an accuracy of 83.5%, 92.4% and 96.5% was

observed for I100, I500 and N179, respectively. The

developed models were then incorporated into an

interactive assessment tool that takes input from the

user via an interactive web form and predicts the

likelihood of one of the three comorbidities in future.

In summary, the study methodology consists of

the following steps: integration of a clinical diabetes

dataset into SQL database, data preprocessing, data

analysis, selection of the input and three predictor

variables for diabetes comorbidities, evaluation of

relative performance of various data mining

algorithms, displaying results on an interactive

dashboard and building an integrated, user-friendly

tool to calculate the risk of developing comorbidities

for individual patients.

There is potential for future research in this area.

For instance, it would be more desirable to have an

exclusive code for recording the type of diabetes and

separate the comorbidities diagnosis of the patients.

Similarly, physicians could identify combinations of

different diagnosis codes for a potentially higher

prediction accuracy due to larger grouping. Finally,

adding time dimension to the metrics could allow a

longitudinal study leading to prediction of timelines

when a comorbidity is likely to occur.

REFERENCES

Anand, R. S., Stey, P., Jain, S., Biron, D. R., Bhatt, H.,

Monteiro, K., Chen, E. S. (2018). Predicting Mortality

in Diabetic ICU Patients Using Machine Learning and

Severity Indices. AMIA Joint Summits on Translation

Science Proceedings, 2018(1), 310-319.

Dagliati, A., Sacchi, L., Tibollo, V., Cogni, G., Teliti, M.,

Martinez-Millana, A., Be, R. (2018). A dashboard-

based system for supporting diabetes care. Journal of

the American Medical Informatics Association, 25(5),

538-547.

Diabetes Canada. (n.d.). Why Federal Leadership Is

Essential Concerning Diabetes. Retrieved Dec 20,

2017, from https://www.diabetes.ca/how-you-can-

help/advocate/why-federal-leadership-is-essential

Heikes, K. E., Eddy, D. M., Arondekar, B., & Schlessinger,

L. (2008). Diabetes Risk Calculator: A Simple Tool for

Detecting Undiagnosed Diabetes and Pre-Diabetes.

Diabetes Care, 5, 1040-1045.

IBM. (2020). SPSS Modeler - Overview. Retrieved

September 22, 2020, from https://www.ibm.com/ca-

en/products/spss-modeler

Kumari, S., & Singh, A. (2013). A data mining approach

for the diagnosis of diabetes mellitus. 2013 7th

International Conference on Intelligent Systems and

Control (ISCO). Coimbatore, India: IEEE.

Lau, M., Campbell, H., Tang, T., J S Thompson, D., &

Elliott, T. (2014). Impact of Patient Use of an Online

Patient Portal on Diabetes Outcomes. Canadian

Journal of Diabetes, 38(1), 17-21.

Lindström, J., & Tuomilehto, J. (2003). The Diabetes Risk

Score: A Practical Tool to Predict Type 2 Diabetes

Risk. Diabetes Care, 26(3), 725-731.

Meng, X.-H., Huang, Y.-X., Rao, D.-P., Zhang, Q., & Liu,

Q. (2013, February). Comparison of three data mining

models for predicting diabetes or prediabetes by risk

factors. The Kaohsiung Journal of Medical Sciences,

29(2), 93-99.

Microsoft. (n.d.). Microsoft SQL documentation. Retrieved

November 1, 2020, from https://docs.microsoft.com/

en-us/sql/?view=sql-server-ver15

Zhang, L., Shang, X., Sreedharan, S., Yan, X., Liu, J., Keel,

S., Wu, J., Peng, W., He, M. (2020). Predicting the

Development of Type 2 Diabetes in a Large Australian

Cohort Using Machine-Learning Techniques:

Longitudinal Survey Study. JMIR MEDICAL

INFORMATICS, 8(7).

Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare