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erage 10.6 ms, Figure 4b) is lower than cloud-based
DT (average 18.8 ms, Figure 4a). Security is impor-
tant to provide secure and reliable services to users,
so it is a metric we should consider in the systems
we develop (Asim et al., 2020). Edge-based systems
are more secure than cloud-based systems because of
their decentralized architecture, whereas cloud-based
systems are more vulnerable to attacks because they
transmit long distances between users and the cloud
(Asim et al., 2020).
6 CONCLUSION
The concept of Industry 4.0, which combines the do-
mains of Informatics and Industry, has spread from
the industrial sector to all other sectors. IoT, 5G and
6G networks, cloud and edge computing, big data, AI
and DT technologies are at the center of these devel-
opments.
In this paper, we discussed the comparison of
cloud-based DT and edge-based DT via a case study.
In this study, we built ML models on PTBD health-
care dataset to predict human heart diseases in real
time and thus to apply quick treatments. In this
context, we performed preprocessing stages such as
cleaning the signal data, denoising, smoothing, peak
extraction, eliminate the NaN values, imputer for
missing values and feature engineering stages such
as feature binning, feature selection, sampling. Even
though in Cardio Twin (Martinez-Velazquez et al.,
2019) paper was obtained the highest precision rate,
we tuning it based on the recall metric because TP
value is more vital in detecting diseases in the health
field. Thus, we outperformed better in terms of recall,
F-score and accuracy.
A major future step of this study is to apply a so-
lution to the data security and privacy concern, which
is frequently encountered in health studies, by com-
bining cloud computing, edge computing, federated
learning and DT technologies. In addition, our future
work also will include on trying different ML mod-
els with the new feature dataset containing the clini-
cal findings of the patients and validating models with
different health dataset. Additionally, a case study in-
cludes data which is collected from sensors can be
added for the real world usage experiment.
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