The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems
Open accessAbstract
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 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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Citations
Cites
Enabling technologies for fog computing in healthcare IoT systems
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Emotion-aware connected healthcare big data towards 5G
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Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges
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EEG-Based Pathology Detection for Home Health Monitoring
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Research works printed but not linked: 2
- 0, Pima Indians Diabetes Database
- 0, Heart Disease Data Set
Cited by
Sorted by citations
The emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366
“The healthcare traffic is forwarded to artificial intelligence (AI) enabled framework to realize a plethora of emerging intelligent healthcare systems, such as disease prediction and remote health monitoring [2].”
BackgroundHealthcare predictive analytics using machine learning and deep learning techniques: a survey
Badawy, M., Ramadan, N., & Hefny, H. A. (2023) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-023-00108-y · Cited by 287
“The authors of [125] presented a novel AI and Internet of Things (IoT) convergence-based disease detection model for a smart healthcare system.”
BackgroundManaging Security of Healthcare Data for a Modern Healthcare System
Almalawi, A., Khan, A. I., Alsolami, F. J., Abushark, Y. B., & Alfakeeh, A. S. (2023) · Sensors · doi:10.3390/s23073612 · Cited by 151
“Therefore, creating integrated healthcare technology can boost productivity and increase patient outcomes at every level of the medical system [4].”
BackgroundSmart Materials Enabled with Artificial Intelligence for Healthcare Wearables
Zheng, Y., Tang, N., Omar, R., Hu, Z., Duong, T. D., Wang, J., Wu, W., & Haick, H. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202105482 · Cited by 148
Metadata
- 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)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 45137-45146
- ISSN
- 2169-3536 (ISSN-L)
- 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
- 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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