Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

Mansour, Romany Fouad; Amraoui, Adnen El; Nouaouri, Issam; Diaz, Vicente Garcia; Gupta, Deepak; Kumar, Sachin

doi:10.1109/access.2021.3066365

Cited by 255Artificial Intelligence in HealthcareInternet of Things and AIECG Monitoring and Analysis

Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

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Mansour, Romany Fouad, Amraoui, Adnen El, Nouaouri, Issam, Diaz, Vicente Garcia, Gupta, Deepak, Kumar, Sachin·2021·IEEE Access

DOI 10.1109/access.2021.3066365

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Abstract

The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both `weights' and `bias' parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.

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Combining IoT, cloud computing and AI have helped researchers with the diagnosis of diabetes and heart disease.
“Combining the IoT, cloud computing and AI have helped researchers with the diagnosis of diabetes and heart disease [208].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [208]

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Research works printed but not linked: 2

I. Azimi, A. Anzanpour, A. M. Rahmani, T. Pahikkala, M. Levorato, P. Liljeberg, and N. Dutt, ''HiCH: Hierarchical fog-assisted computing architecture for healthcare IoT,'' ACM Trans. Embedded Comput. Syst., vol. 16, no. 5s, p. 174, Oct. 2017.
ADNEN EL AMRAOUI received the Ph.D. degree in process control from the Technical Univer- sity of Belfort-Montbéliard (UTBM), France, in 2011. From September 2013 to August 2014, he was a Postdoctoral Researcher with the French Engineering School ''Ecole Centrale de Lille (EC-Lille).'' From September 2014 to August 2018, he was an Associate Professor with the University of Orléans, France. Since September 2018, he has been an Associate Professor with the University of Artois, France. He is the first and the corresponding author of several pub- lications in refereed journals, such as COR, CIE, EEAI, EJIE, 4-OR, JSSSE, SIC, and so on, and in several IEEE and IFAC conferences. His research interests include combinatorial optimization, artificial intelligence, approxi- mation, interval analysis, scheduling and production planning, supply chain management, transportation systems, and maintenance.

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Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems
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Authors: Mansour, R. F., Amraoui, A. E., Nouaouri, I., Diaz, V. G., Gupta, D., & Kumar, S.
Affiliations: New Valley University (EG)
Université d'Artois (FR)
South Ural State University (RU)
ORCID: Mansour, Romany Fouad
Amraoui, Adnen El
Nouaouri, Issam
Gupta, Deepak
Kumar, Sachin
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021
Volume: 9
Pages: 45137-45146
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2021.3066365
OpenAlex: W3148433189
Cited by: 255
References: 20
Citations by year: 2026: 23 · 2025: 70 · 2024: 60 · 2023: 55 · 2022: 38 · 2021: 9
FWCI: 35.30
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: IEEE Access (publishedVersion, cc-by-nc-nd)
HAL (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Ministry of Science and Higher Education of the Russian Federation (FENU-2020-0022)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Keywords: disease diagnosis, smart healthcare, Internet of Things, artificial intelligence, heart disease, diabetes mellitus, crow search optimization, long short-term memory, parameter tuning, Isolation Forest, outlier removal, wearable sensors

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Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

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