Citation: Oyeleye, M.; Chen, T.;
Titarenko, S.; Antoniou, G. A
Predictive Analysis of Heart Rates
Using Machine Learning Techniques.
Int. J. Environ. Res. Public Health 2022,
19, 2417. https://doi.org/10.3390/
ijerph19042417
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 25 January 2022
Accepted: 15 February 2022
Published: 19 February 2022
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International Journal of
Environmental Research
and Public Health
Article
A Predictive Analysis of Heart Rates Using Machine
Learning Techniques
Matthew Oyeleye, Tianhua Chen * , Sofya Titarenko and Grigoris Antoniou
Department of Computer Science, School of Computing and Engineering, University of Huddersfield,
Huddersfield HD1 3DH, UK; matthew.oyeleye@hud.ac.uk (M.O.); s.titarenko@hud.ac.uk (S.T.);
g.antoniou@hud.ac.uk (G.A.)
* Correspondence: t.chen@hud.ac.uk
Abstract:
Heart disease, caused by low heart rate, is one of the most significant causes of mortality in
the world today. Therefore, it is critical to monitor heart health by identifying the deviation in the
heart rate very early, which makes it easier to detect and manage the heart’s function irregularities at
a very early stage. The fast-growing use of advanced technology such as the Internet of Things (IoT),
wearable monitoring systems and artificial intelligence (AI) in the healthcare systems has continued
to play a vital role in the analysis of huge amounts of health-based data for early and accurate
disease detection and diagnosis for personalized treatment and prognosis evaluation. It is then
important to analyze the effectiveness of using data analytics and machine learning to monitor and
predict heart rates using wearable device (accelerometer)-generated data. Hence, in this study, we
explored a number of powerful data-driven models including the autoregressive integrated moving
average (ARIMA) model, linear regression, support vector regression (SVR), k-nearest neighbor
(KNN) regressor, decision tree regressor, random forest regressor and long short-term memory
(LSTM) recurrent neural network algorithm for the analysis of accelerometer data to make future
HR predictions from the accelerometer’s univariant HR time-series data from healthy people. The
performances of the models were evaluated under different durations. Evaluated on a very recently
created data set, our experimental results demonstrate the effectiveness of using an ARIMA model
with a walk-forward validation and linear regression for predicting heart rate under all durations and
other models for durations longer than 1 min. The results of this study show that employing these
data analytics techniques can be used to predict future HR more accurately using accelerometers.
Keywords: heart rate; accelerometer; time series; data analytics; machine learning
1. Introduction
According to the World Health Organization (WHO), heart disease (HD), also known
cardiovascular disease (CVD), is one of the major causes of mortality in the world today [
1
].
It reported that 17.9 million people were estimated to have died from CVDs in 2019,
accounting for 32% of all global deaths. Heart disease describes a series of conditions that
affect the heart, which in turn affects the heart to pump blood around the body normally [
2
].
However, there is no way to track cardiovascular or heart disease without considering the
heart rate (HR), which is one of the important measures of heart health. The HR is the
number of times the heart’s chambers contract (squeeze) and relax to pump blood within a
specified period (i.e., minute) and at rest, a normal heart beats approximately 60–80 times
per minute [
2
]. The heart rate, however, is affected by the activities a human engages in and
in turn, the heart rate data are nonstationary in nature, which are unpredictable and cannot
be modelled or forecasted [
3
,
4
]. This may be complicated by unpredictability attributes
and other behavioral risk factors such as tobacco use, unhealthy diet and obesity, physical
inactivity and harmful use of alcohol, which contribute to worse wellbeing and may even
double the death risk of a CVD patient [
4
,
5
]. It is then important to detect cardiovascular
disease as early as possible.
Int. J. Environ. Res. Public Health 2022, 19, 2417. https://doi.org/10.3390/ijerph19042417 https://www.mdpi.com/journal/ijerph
