Short-term Glucose Prediction based on Oral Glucose Tolerance Test

Values

Elias Dritsas, Sotiris Alexiou, Ioannis Konstantoulas and Konstantinos Moustakas

Department of Electrical and Computer Engineering, University of Patras, 26504 Rion, Greece

Keywords:

OGTT, Glucose, Diabetes, Regression, Short-term Prediction.

Abstract:

Abnormal glucose metabolism increases the risk for cardiovascular disease and mortality. A key motivation for

investigating this topic is Diabetes prevalence, which is the most common example of metabolic disorder that

concern humans all over the world. The oral glucose tolerance test (OGTT) constitutes a traditional medical

screening tool for all types of diabetes such as prediabetes, gestational, type 2 diabetes, insulin resistance

or discrimination of Impaired Glucose Tolerance (IGT) from Natural Glucose Tolerance (NGT) individuals.

Another motivation for this study is that a plethora of studies has shown the effectiveness of machine learning

in glycemic control and improvement of diabetic’s management. This research study aims to evaluate the

adequacy of machine learning on the short-term prediction of glucose levels. The main contribution of this

analysis is a Random Forest regression tree model which, has been trained considering various risk factors and

glucose samples obtained by a 2-hour OGTT, after a fast and then after an oral intake of glucose, at intervals

of 30 minutes. The research outcomes verify the efﬁcacy of Random Forest (RF).

1 INTRODUCTION

The analysis of blood glucose levels is a crucial task

for the assessment of glucose metabolic control and

the deﬁnition of the therapeutic protocol. Patients

with Impaired Glucose Tolerance (IGT) are more

likely to have type 2 diabetes (Knowler et al., 2009),

(Fiorentino et al., 2015) and are at a higher car-

diovascular disease risk (Abdul-Ghani et al., 2017),

(Baranowska-Jurkun et al., 2020). IGT in the litera-

ture is commonly deﬁned as a cutoff of 7.8 mmol/L

of the plasma glucose levels measured after spe-

ciﬁc strenuous physical activity and 2 hours after an

Oral Glucose Tolerance Test (OGTT) of 75g of glu-

cose (World-Health-Organization, 1999), (Kerner and

uckel, 2014). As the most common method of test-

ing glucose tolerance, the OGTT is used to screen for

diabetes like type 2, prediabetes and gestational dia-

betes. The OGTT provides data that can also quan-

tify insulin sensitivity versus tolerance (Altuve et al.,

2016). These types of diabetes may be accountable

for either long-term, such as kidney disease, heart dis-

ease, stroke, or short-term, like, hyperglycemia or hy-

poglycemia. Hence, the early identiﬁcation of undi-

agnosed diabetic patients or those at high risk is an

emergency.

Machine learning, as a tool in data science, has

seen major successes in the healthcare sector (Bide

and Padalkar, 2020). The availability of data and the

quality and quantity of them increase the accuracy of

any data-driven approach of the ﬁeld. In the case of

blood glucose quantiﬁcation and, more generally, di-

abetes risk monitoring, more data becomes available

each year. In this research area, there is an increase

in interest (Islam et al., 2021), (Refat et al., 2021) and

there is no doubt that machine learning can be used

as an evaluation tool of blood glucose measurement

datasets.

In the literature, we have seen that machine learn-

ing can be used for predicting glucose levels dur-

ing or after an OGTT (Maeta et al., 2018) improv-

ing the quantity and quality of data in datasets asso-

ciated with glucose, insulin and diabetes in general.

While improving the data of a dataset, in general, is

a very useful science, more speciﬁcally, predicting

risk for diabetes in individuals improves health and

well-being, as prevention and prediction are usually

instrumental to better treatment. For this reason, in

this work, we aim to apply machine learning and data

science principles to implement a method that could

predict the risk of diabetes in individuals.

Body mass index (BMI) is a risk factor that is

utilized by experts for the identiﬁcation of over-

Dritsas, E., Alexiou, S., Konstantoulas, I. and Moustakas, K.

Short-term Glucose Prediction based on Oral Glucose Tolerance Test Values.

DOI: 10.5220/0010974200003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 249-255

ISBN: 978-989-758-552-4; ISSN: 2184-4305

249

weight or obese individuals given that these groups

are more prone to occur insulin resistance and dia-

betes (Charoentong et al., 2004). Besides, BMI is

strictly connected with the individual weight, which,

in turn, is impacted by physical activity, meals pattern

and general lifestyle. Of course, the list of diabetes-

related risk factors is not restricted to them. In the

context of this paper, a naive methodology is pre-

sented that could be used for the short-term risk pre-

diction of IGT, gestational diabetes or type 2 diabetes

(as shown in Table 1 (Rong et al., 2021)) based on his-

torical glucose values obtained from an OGTT. As a

ﬁrst approach, a random forest regression tree is em-

ployed with satisfactory prediction performance.

The elaborated method for the short-term predic-

tion of OGTT glucose levels is being developed in-

dependently with publicly available data and, in par-

allel, as part of the SmartWork project with data

pending from pilots of the project. The SmartWork

project (Fazakis et al., 2021) is a distributed system

of e-health management and aims to improve an indi-

vidual’s quality of life through serving interventions

and suggestions based on biometric data and analysis

done on said data. This work will be integrated into

the wider health improvement interventions and sug-

gestions system of the SmartWork system. This work

is also to be integrated in the GATEKEEPER architec-

ture, as the aim of the project is to provide smart solu-

tions for early risk detection and prevention among

the senior population. The GATEKEEPER project

is an e-health ecosystem to enable collaboration be-

tween healthcare providers, industry and public ad-

ministration.

The next sections of this paper are organized as

follows. In Section 2, the blood glucose sampling

scheme is introduced. Next, the proposed machine

learning-based methodology is presented. In Section

3, the necessary details on the evaluated dataset are

given. Section 5 presents experiments information,

implementation details and deﬁnitions of the perfor-

mance metrics. Section 6 makes a critical review and

discussion on the results. Finally, Section 7 summa-

rizes the main outcomes of the analysis and future re-

search directions.

2 DATASET

For the purpose of the current analysis, we considered

the dataset derived by the authors in (Edinburgh et al.,

2018). Therefore, we will present some useful infor-

mation concerning the preparation made before the 2-

h OGTT, as shown in Figure 1, under three different

conditions.

Table 1: OGTT results in mmol/L according to the Ameri-

can Diabetes Association (ADA) criteria.

No Diabetics IGT Diabetics

Fasting

value

< 6 6.0 −7.0 > 7.0

(before

test)

At 2

hours

< 7.8 7.9 − 11.0 > 11.0

2.1 Blood Glucose Sampling Scheme

Participants arrive at the laboratory with a deviation of

one hour before or after 08:00 in the morning. After

taking a 5 minute expired gas sample and a baseline

muscle sample from vastus lateralis, the Breakfast-

Excercise (BE) and Breakfast-Rest (BR) participant

groups consume a 431 kcal porridge breakfast while

the Fasting-Excercise (FE) group is allowed only wa-

ter, at that point and every 60 minutes, therefore, both

groups have expired gas samples taken.

After 1 hour 40 minutes of rest a [6.6

] glu-

cose infusion is initiated on the participants. After 20

minutes, BE and FE participants initiate a 60 minute

cycling exercise at 50% Peak Power Output (PPO)

on an ergometer, BR participants rest instead of ex-

ercising, while both groups have expired gas samples

taken every 15 minutes and blood samples every 40 to

50 minutes. Then a 2-h OGTT is done, with arterial-

ized blood sampling every 10 minutes and expired gas

sampling every 60 minutes. The administered OGTT

is 73g of glucose. A ﬁnal sample is taken 2 hours after

the start of the OGTT.

2.2 Features Description

Participants features include age, height and body

mass which are measured in years, centimetres (cm)

and kilograms (Kg), respectively. An important

feature-risk factor that relates to obesity is body mass

index (BMI) which is calculated as the body mass

divided by squared height (Kg/m

). Moreover, the

Fat mass is measured, in Kg, by a whole-body dual-

energy x-ray absorptiometry scan and, the fat mass

index is calculated as the ratio of fat mass divided by

squared height (kg/m

). Body fat percentile is cal-

culated as fat mass divided by body mass and fat-

free mass is calculated as subtraction of fat mass from

body mass.

Peak power output is measured in Watt and cal-

culated as the work rate ﬁnal stage on an endurance

workout stress test with increasing intensity, plus a

fraction of time spent in the other stages multiplied

by their respective work rate increment. HRmax is the

HEALTHINF 2022 - 15th International Conference on Health Informatics

250

Figure 1: Blood Glucose Sampling Scheme(Edinburgh et al., 2018).

maximum heart rate measured throughout all the exer-

cises. VO

peak measures VO

and is calculated as the

highest average VO

over a 30 second period. Blood

glucose is measured in millimoles per litre (mmol/L)

by administering an OGTT at different points during

the day.

For each participant, the dataset consists of age,

height, body mass, body mass index, fat mass, fat

mass index, body fat percentile, fat-free mass, peak

power output, HRmax which are single values. Blood

glucose is measured every 10 minutes with differen-

tiation to before and after exercises of varying inten-

sity creating a discrete series of results for the subject

throughout the timeline of the study.

2.3 Dataset Preprocessing

In machine learning, feature selection is a key com-

ponent in developing accurate and trustworthy pre-

diction models. It is well known that the correlation

coefﬁcient of the prediction improves as the attributes

dimension increases until the optimal number of fea-

tures is obtained. Therefore to avoid overﬁtting and

to achieve better prediction results we used the most

correlated attributes according to an attribute ranker,

which is a proven technique for selecting the most rel-

evant attributes from a dataset based on the Pearson’s

correlation coefﬁcient (CC) (Mukaka, 2012) deﬁned

CC =

∑

i=1

− ¯x)(y

− ¯y)

∑

i=1

− ¯x)

∑

− ¯y)

, (1)

where x

and y

are the values of features x and y

for the i-th individual. More speciﬁc, the attribute

ranker only selects attributes that have a moderate-to-

high positive or negative correlation (close to -1 or 1)

and drops any attributes with a low correlation (value

close to zero).

For the purposes of the speciﬁc OGTT experi-

ment, a publicly available dataset consisting of mostly

invasive data had been engaged. In the ﬁnal dataset,

which is a subset of the AJP dataset, we included all

participants’ demographic, anthropometric and clin-

ical data like age (years), stature (cm), body mass

(kg), body mass index (kg/m2), fat mass (kg), fat

mass index (kg/m2), body fat (%), fat-free mass (kg),

peak(l/min), VO

peak (ml/ kg·min), peak power

output (W), HRmax (beats/min) and oral glucose tol-

erance test glucose samples for each participant, one

every 10 minutes for each one of the three trials: BR,

BE, and FE. A statistical description of the dataset is

outlined in Table 2. Notice that two of the twelve par-

ticipants belong to the overweight class (25 ≤ BMI <

30) while the rest are healthy (18.5 ≤ BMI < 25)

(Kakoly et al., 2019).

3 METHODOLOGY

In the context of this analysis, the aim is to present a

methodology for short-term glucose prediction based

on OGTT values and make lifestyle interventions to

avoid either short- or long-term effects of diabetes-

Short-term Glucose Prediction based on Oral Glucose Tolerance Test Values

251

Table 2: Dataset Statistical Characteristics.

Feature Mean ± std Min Max

Age 22.6 ± 2.8 21 26

BMI 23.5 ± 1.9 21.1 27.2

Fat Mass Index 3.27 ± 1.13 1.7 5.6

Body Fat % 13.76 ± 4.44 8 23.6

PPO 317.17 ± 66.97 200 421

HR max 189.3 ± 10.40 170 206

VO2peak 3.99 ± 0.72 2.65 5.06

VO2peak2 53.09 ± 9.85 37 70.8

related metabolic disorder. In particular, machine

learning has been employed for the prediction of

OGTT values. Here, it should be noted that it

is a widely used test suitable for the identiﬁca-

tion/diagnosis of pre-diabetes, gestational diabetes in

pregnant women, insulin resistance and reactive hy-

poglycemia.

The prediction model was trained with the fea-

tures of twelve healthy and physically active men

(that obtained from the publicly available dataset de-

scribed in Section 2.3), such as twelve glucose mea-

surements collected from an OGTT test, BMI, heart

rate and exercise-related parameters. The role of

breakfast meal, fasting and exercise on how the body

metabolises the intake of sugar/carbohydrate is also

assessed. The application of this model aims to

support health care management. In the context of

this study, the forecasting performance of a machine

learning model is presented under different cases as

described in Section 2. In particular, the RF tree is uti-

lized to estimate the upcoming glucose values (Alex-

iou et al., 2021) of each participant by constructing a

global model.

The RF method builds prediction models using

regression trees, which are usually unpruned to give

strong predictions. The bootstrap sampling method

is used on the regression trees which should not be

pruned. Only the optimal nodes are sampled to form

the optimal splitting feature. The random sampling

technique used in selecting the optimal splitting fea-

ture lowers the correlation and hence, the variance of

the regression trees. It improves the predictive capa-

bility of distinct trees in the forest. The sampling us-

ing bootstrap also increases independence among in-

dividual trees (Denil et al., 2014), (Ye et al., 2020).

Bagging and random feature selection are two

powerful machine-learning techniques used by RF.

Each tree is trained on a bootstrap sample of the train-

ing data in bagging, and predictions are made by a

majority vote of the trees. RF is a step forward from

bagging. When developing a tree, RF randomly se-

lects a subset of features to divide at each node rather

than using all of them. It uses out-of-bag (OOB) sam-

Table 3: Model Hyperparameters.

Algorithm Parameters

Random

Forests

Size of each bag = 100%

Maximum tree depth = Unlimited

Number of iterations = 100

ples to do a type of cross-validation in tandem with

the training process to check the RF algorithm’s pre-

diction ability. Speciﬁcally, each tree is generated us-

ing a unique bootstrap sample during the training pro-

cess. Some sequences will be ’left out’ of the sam-

ple, while others will be repeated in the sample, be-

cause bootstrapping involves sampling with replace-

ment from the training data. The OOB sample is

made up of the sequences that were left out. OOB

sequences can be utilized to measure prediction per-

formance because they were not used in tree construc-

tion (Khan et al., 2021).

4 EXPERIMENTS SETUP

The data preprocessing was evaluated using WEKA

and Stata V.14 tool kits. WEKA is a JAVA-based data

mining toolkit created at the University of Waikato in

New Zealand. It’s a free software tool distributed un-

der the GNU General Public License. WEKA toolkit

provides a large library of methods and models for

classiﬁcation, clustering, prediction, attribute selec-

tion, and data display after an investigation. Stata

a general-purpose statistical software package devel-

oped by StataCorp for data manipulation, visualiza-

tion, statistics, and automated reporting. Stata has al-

ways employed an integrated command-line interface

and can import data in a variety of formats including

ASCII data formats.

For the purposes of the speciﬁc experiment, we

developed a regression tree model using a machine

learning algorithm whose parameters are illustrated

in Table 3. The random Forest R package was used

to develop the prediction model. We also used as in-

put the 23 most important attributes according to the

ranking selection method. In addition, we evaluate the

effectiveness of the Random Forest regression tree,

considering mean squared error (RMSE) and mean

absolute error (MAE) (Mohebbi et al., 2020) as per-

formance metrics of the prediction model.

https://www.cs.waikato.ac.nz/ml/weka/

https://www.stata.com/

HEALTHINF 2022 - 15th International Conference on Health Informatics

252

Table 4: Glucose Prediction evaluation under 3 meal-

exercise cases.

Performance Random Forest

Metrics BR BE FE

CC 0.785 0.867 0.709

MAE 1.13 0.901 0.852

RMSE 1.47 1.22 1.07

0 20 40 60 80 100 120

Sampling time-minutes

OGTT Values-mmol/L

Average Actual BR

Average Predicted BR

Average Actual BE

Average Predicted BE

Average Actual FE

Average Predicted FE

Figure 2: Avearage OGTT values prediction under 3 meal-

exercise cases.

MAE(g

) =

∑

i=1



i, j

− ˆg

i, j



(2)

RMSE(g

) =

∑

i=1

i, j

− ˆg

i, j

)

(3)

Considering the methodology previously de-

scribed in Section 3, the experiments’ settings and

performance evaluators presented here, in the follow-

ing section, we will demonstrate the obtained research

outcomes.

5 RESULTS AND DISCUSSION

Delayed meals and exercise are two factors that in-

crease the risk of hypoglycemias. The investigated

dataset examines the impact of two workout (rest, cy-

cling exercise) and meal (breakfast, fasting) patterns

on glycaemic control based on the OGTT 1h post the

rest/exercise session. It should be emphasised that the

current problem in monitoring glucose supply is the

control of eating habits of the participants, which are

the basis of healthy living — with or without diabetes.

However, for those who have been diagnosed with di-

abetes, it is important to know how food affects their

blood glucose levels.

20 40 60 80 100 120

Sampling time-minutes

OGTT Values-mmol/L

Participant 10

Actual-BR

Predicted-BR

Actual-BE

Predicted-BE

Actual-FE

Predicted-FE

Figure 3: Participant 10 OGTT values prediction 3 meal-

exercise cases.

In Table 4, we summarize the machine learning

model performance in terms of three metrics under

three different trials, as discussed above (see Section

2). The curves in Figure 2 depict the average actual

and predicted values by the Random Forest regressor.

These curves were drawn by plotting the time-course

change of glucose concentrations during an OGTT on

a 2-h interval, 0 to 120 minutes, with a sampling rate

of 1 sample per 10 minutes. From the relevant lit-

erature, the shape of the glucose response curve is

monophasic (Kim et al., 2016).

Observing Figure 2, we see higher reductions in

glucose levels combined with 60 min exercise and/or

fasting diet. Also, the improved (lower) glucose con-

centrations at 30 min post-OGTT were associated

with exercise. Figure 3 focuses on a speciﬁc partic-

ipant. The glucose pattern of that user follows the

average behaviour.

The main purpose of this analysis is to mon-

itor OGTT glucose to prevent the future develop-

ment or delay the complications of diabetes (Alyass

et al., 2015). The collection and forecasting of OGTT

data will help assess the body’s ability to use glu-

cose, screen diabetes, and make interventions rec-

ommended by primary care groups, such as person-

alized health advice and digital coaching informa-

tion. The intervention may also strengthen the self-

management of those diagnosed with diabetes and

promote healthy habits. Finally, this methodology

will be part of the Artiﬁcial Intelligence (AI) services

of the SmartWork and GATEKEEPER architecture

to improve the independence and ability of the older

people where diabetes disease is more prevalent.

Short-term Glucose Prediction based on Oral Glucose Tolerance Test Values

253

6 CONCLUSIONS

In conclusion, our work shows that machine learning

is capable of making a short-term prediction of the

OGTT glucose values. The outcomes of the study

may provide useful support to health care providers

in early detection of diabetes, making more informed

decisions for the prevention of serious consequences

and overall management of diabetes.

A limitation of this research paper is its small sam-

ple of historical OGTT data. Hence, to establish a

more accurate and reliable prediction model follow-

up OGTT data should be considered in the analysis.

As future work, we aim to evaluate the perfor-

mance of more regression models like Support Vector

Machine (SVM) and Neural Networks. Furthermore,

our purpose is to investigate the OGTT data from in-

dividuals diagnosed with either diabetes or IGT. Fi-

nally, it would be challenging to study the usefulness

of machine and/or deep learning on the same prob-

lem on elderly individuals, women with gestational

diabetes (de Wit et al., 2019) and, also emphasize the

shape of the OGTT glucose curves since the shape

has been used as a predictor of treatment outcomes

(Jagannathan et al., 2020).

ACKNOWLEDGEMENTS

This work has been partially supported by the

European Union’s H2020 research and innovation

programme SmartWork under grant agreement No

826343, SC1-DTH-03-2018 - Adaptive smart work-

ing and living environments supporting active and

healthy ageing and GATEKEEPER under grant

agreement No 857223, SC1-FA-DTS-2018-2020 -

Smart living homes-whole interventions demonstra-

tor for people at health and social risks.

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