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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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“[12] 2021 No CSOA and LSTM Disease Diagnosing Framework Scalability and consistency”
BackgroundMethodTarget Detection Algorithm in Crime Recognition Using Artificial Intelligence
Shin, H. K., Kim, H. Y., & Lee, S. H. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.021185 · Cited by 8
Deep Forest Based Internet of Medical Things System for Diagnosis of Heart Disease
Askar, S. K. (2023) · ARO-The Scientific Journal of Koya University · doi:10.14500/aro.11174 · Cited by 8
Alharbi, E., Cherif, A., & Nadeem, F. (2023) · Smart Cities · doi:10.3390/smartcities6050130 · Cited by 8
Implementation of IoT and AI based Device to Monitor Entry and Exit Points in Hospitals
Shukla, K. K., Jaiswal, M., Sharma, B., Sharma, D., Jain, A., & Pradhan, A. (2024) · International Conference on Data Intelligence and Cognitive Informatics (ICDICI) · doi:10.1109/icdici62993.2024.10810926 · Cited by 7
Analyzing the Futuristic Scope of Artificial Intelligence in the Healthcare Sector in India
Joshi, R., Pandey, K., Kumari, S., Gupta, S. K., Mohanty, M., & Salau, A. O. (2024) · International Conference on Computational and Characterization Techniques in Engineering & Sciences (IC3TES) · doi:10.1109/ic3tes62412.2024.10877568 · Cited by 7
Ramirez-Bautista, J. A., Chaparro-Cárdenas, S. L., Esmer, C., & Huerta-Ruelas, J. A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105463 · Cited by 7
CDH1 gene as biomarker towards breast cancer prediction*
Banerjee, S., Sengupta, A., Ghosh, S. K., & Banerjee, R. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2316770 · Cited by 7
“Machine learning techniques are often useful in understanding hidden patterns in a data set, and to get insights for precise estimation in the field of disease diagnosis (Ao et al., 2020; Mansour et al., 2021; Awwalu et al., 2015; Li et al., 2020; Kha et al., 2023; Yuan et al., 2023).”
Faustin, U. M., & Zou, B. (2022) · Computation · doi:10.3390/computation10070104 · Cited by 7
“Simple traditional GA starts with a diverse population after a small number of generations converged to a certain point where each chromosome will contain a similar solution [22].”
BackgroundNishith, V. S. S. P., Saha, A., & Patwari, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106567 · Cited by 6
Intrusion Detection System for Defending against DoS Attacks in the IoMT Ecosystem
Puthin Krishna, M., Kumar, S. R., & Palani Thanaraj, K. (2023) · International Conference on Communication, Computing and Industry 4.0 (C2I4) · doi:10.1109/c2i659362.2023.10430773 · Cited by 6
“This transformative technology holds the promise of improving patient outcomes, enhancing the quality of care, and reducing healthcare costs[2].”
BackgroundA Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment
Verma, A., Gupta, A. K., Kumar, V., Rajak, A., Kumar, S. G., & Panda, R. N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19131-w · Cited by 6
“Also, Mansour et al. [33] presented the crow search-based LSTM model for analyzing IoT healthcare data.”
Enabling Technologies in IoMT Smart Healthcare: A Survey
Shivagangatharani, B., & Rabiya, M. S. (2024) · doi:10.1109/iccsp60870.2024.10544072 · Cited by 6
“The iForest techniques was used for outlier detection step [5].”
MethodKumar, A., Verma, D. K., & Dwivedi, R. K. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113209 · Cited by 7
Dynamic Multimodal Fusion for Real-Time Lung Cancer Diagnostics in IoMT-Enabled Healthcare
Ping, W., Sun, Q., Wang, Q., Khan, F., Alturki, R., Alshawi, B., & Zhang, N. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3576482 · Cited by 6
Liu, H., Luo, Q., Shao, M., Pan, J., & Li, J. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/9310558 · Cited by 6
Empowering Artificial Intelligence of Things (AIoT) Toward Smart Healthcare Systems
Malik, A., Parihar, V., Bhawna, Bhushan, B., & Karim, L. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-31952-5_6 · Cited by 6
Thumilvannan, S., & Balamanigandan, R. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-232851 · Cited by 6
Artificial Intelligent Management for Internet of Things: A Review
Altaie, R. H., & Hoomod, H. K. (2022) · International Conference on Current Research in Engineering and Science Applications (ICCRESA) · doi:10.1109/iccresa57091.2022.10352510 · Cited by 6
“Mobile phones can be used as sensors that collect data and share it between vehicles and traffic monitoring [31]-[34].”
BackgroundPadma, A., Ramaiah, M., & Ravi, V. (2025) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-025-00254-2 · Cited by 10
Babu, P. A., & Babu, B. S. (2025) · Cybernetics & Systems · doi:10.1080/01969722.2025.2577925 · Cited by 5
Pal, P., & Das, S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.300 · Cited by 5
Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical
Mansour, R. F., & Althobaiti, M. M. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026515 · Cited by 5
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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