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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
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- 0, Heart Disease Data Set
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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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