Citation: Alotaibi, M.; Alotaibi, S.S.
Optimal Disease Diagnosis in
Internet of Things (IoT) Based
Healthcare System Using Energy
Efficient Clustering. Appl. Sci. 2022,
12, 3804. https://doi.org/10.3390/
app12083804
Academic Editors: Keun Ho Ryu,
Nipon Theera-Umpon and
Andrea Prati
Received: 2 February 2022
Accepted: 29 March 2022
Published: 9 April 2022
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Article
Optimal Disease Diagnosis in Internet of Things (IoT) Based
Healthcare System Using Energy Efficient Clustering
Majid Alotaibi
1,
* and Saud S. Alotaibi
2
1
Department of Computer Engineering, College of Computer and Information Systems,
Umm Al-Qura University, Makkah 21955, Saudi Arabia
2
Department of Information Systems, College of Computer and Information Systems,
Umm Al-Qura University, Makkah 21955, Saudi Arabia; ssotaibi@uqu.edu.sa
* Correspondence: mmgethami@uqu.edu.sa
Abstract:
This paper aims to introduce a novel approach that includes three steps, namely Energy
efficient clustering, Disease diagnosis, and an Alert system. Initially, energy-efficient clustering of
nodes was conducted, and to render the clustering more optimal, its centroid was optimally selected
by a new hybrid algorithm. In addition, this cluster formation was conducted based on constraints
such as distance and energy. Further, the disease diagnosis in IoT was performed under two phases
namely, “feature extraction and classification”. During feature extraction, the statistical and higher-
order features were extracted. These extracted features were then classified via Optimized Deep
Convolutional Neural Network (DCNN). To make the classification more precise, the weights of
the DCNN were optimally tuned by a new hybrid algorithm referred to as Hybrid Elephant and
Moth Flame with Adaptive Learning (HEM-AL). Finally, an alert system was enabled via proposed
severity level estimation, which determined the severity of the disease, suggesting patients to visit
the hospital. Lastly, the supremacy of the developed approach was examined via evaluation over the
other extant techniques. Accordingly, the proposed model attained an accuracy of 0.99 for test case 1,
and was 7.41%, 17.34%, and 13.41% better than traditional NN, CNN, and DCNN models.
Keywords: IoT; clustering; disease diagnosis; alert system; HEM-AL algorithm
1. Introduction
The current explosion of Information and Communication Technologies (ICT) and
embedded systems represents the introduction of a novel technology: Internet of Things
(IoT). It allows objects and individuals in virtual environments and the physical world to
interrelate with one another [
1
,
2
]. A considerable number of appliances deploying IoT as
a major data collection element, form smarter environments such as smart cities, homes,
healthcare, and smart transportation [
3
]. The amalgamation of IoT and cloud-oriented
online appliances perform better than usual cloud-oriented appliances with respect to
effectiveness [
4
–
6
]. The rising number of appliances in industries such as banking, military
and the medical field can employ this amalgamation. Particularly, the cloud-oriented
IoT helps offer proficient services to health care appliances for accessing and monitoring
records from distant locations.
The healthcare industry has shown substantial growth in recent years, contributing
significantly to revenue and employment. In the past few years, the diagnosis of diseases
and abnormalities in the human body was possible only after having a physical examination
in the hospital. Most of the patients remained in the hospital throughout their treatment
process, which resulted in higher healthcare costs and strain on rural and remote health
facilities. Through the technological advancements achieved over time, it is now possible
for miniaturized devices such as smartwatches to diagnose various diseases and monitor
their health.
Appl. Sci. 2022, 12, 3804. https://doi.org/10.3390/app12083804 https://www.mdpi.com/journal/applsci
