Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities
Abstract
:1. Introduction
- A new paradigm for smart healthcare services in smart cities using IoT and cloud architecture is proposed. The following key features were developed to ensure data time sensitivity and consistent dissemination: the decision tree (DT) method is adopted to classify and segregate the time-sensitivity data; prioritization of access and segregation of sensitive features is developed; assigning priorities based on the time sensitivity is customized and incorporated in the ICDS framework.
- The simulation environment is developed to validate the proposed model by populating end-to-end data. A sequential increment snippet program is introduced to analyze the proposed work.
- The performance of the ICDS is compared with two other landmark schemes in which the ICDS enhanced service distribution and availability by 9.03% and 8.91%, respectively, and reduced waiting time, allocation time, and failures by 11.77%, 9.46%, and 8.61%, respectively.
2. Related Works
Study | Platform/Model | Aim | Key Features | Impact on Healthcare |
---|---|---|---|---|
Xu et al. [12] | CSCP | Sleep disorder diagnosis and management | Device-agonistic, uses SCAN and data analytics | Increases accuracy and efficiency of sleep disorder detection |
Sabokbar et al. [13] | FIS-based spatial model | Accessibility measurement in healthcare centers | Evaluates and addresses healthcare services provided to patients | Increases the accuracy of accessibility predictions and improves healthcare service accessibility |
Abdelmoneem et al. [14] | Mobility-aware heuristic-based scheduling and allocation approach | Allocate resources in healthcare centers | Uses cloud and fog computing architecture, detects spatial and temporal factors | Reduces computation latency and energy consumption to improve the performance and effectiveness of healthcare centers |
Gouveia et al. [15] | CTSP | Optimization and computation in healthcare applications | Solves traveling salesman problems, imposes positional consistency constraints | Reduces latency and improves efficiency and performance of healthcare services |
Zgheib et al. [16] | Scalable semantic framework for IoT-based healthcare | Identify patients’ daily activities for diagnosis | Uses semantic reasoning technique, detects complex data for database | Increases in detection and prediction accuracy improve the feasibility and mobility of IoT-based healthcare applications |
Waibel et al. [19] | Tiers of service framework for children’s healthcare | Provide operational planning for healthcare centers | Identifies key factors and symptoms in the database, organizes services | Maximizes quality and feasibility of services provided to patients |
Li et al. [20] | Q-learning algorithm-based optimal scheduling approach | Scheduling in cloud healthcare systems | Solves optimization problems, reduces cost and time consumption | Increases accuracy of scheduling, enhances the performance and effectiveness of healthcare systems |
Elshahed et al. [21] | PSTBA | Healthcare monitoring systems | Uses virtual machine, reduces expected processing time by performing specific functions | Achieves high efficiency in providing healthcare services compared to traditional methods |
3. Materials and Methods
3.1. Information-Centric Discrimination Scheme
3.2. Decision-Tree (DT) Algorithm
3.2.1. Classification Based on Time
3.2.2. Classification Based on Access
3.2.3. Assigning Priority
3.3. Allocation Process
4. Results and Discussion
4.1. Service Distribution
4.2. Service Availability
4.3. Waiting Time
4.4. Allocation Time
4.5. Failure
Metrics | SSF | PSTBA | ICDS |
---|---|---|---|
Service Distribution (%) | 91.673 | 93.513 | 95.602 |
Service Availability | 0.954 | 0.968 | 0.9907 |
Waiting Time (s) | 2.375 | 1.452 | 0.5722 |
Allocation Time (s) | 3.43 | 2.28 | 1.234 |
Failures | 0.075 | 0.054 | 0.0358 |
Metrics | SSF | PSTBA | ICDS |
---|---|---|---|
Service Distribution (%) | 90.555 | 92.941 | 95.659 |
Service Availability | 0.949 | 0.967 | 0.9995 |
Waiting Time (s) | 2.311 | 1.532 | 0.5241 |
Allocation Time (s) | 3.45 | 2.61 | 1.189 |
Failures | 0.076 | 0.047 | 0.0362 s |
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhang, J.; Yang, Y.; Liu, X.; Ma, J. An efficient blockchain-based hierarchical data sharing for Healthcare Internet of Things. IEEE Trans. Ind. Inform. 2022, 18, 7139–7150. [Google Scholar] [CrossRef]
- Blanken, M.; Mathijssen, J.; Nieuwenhuizen, C.V.; Raab, J.; Oers, O.V. Actors’ awareness of network governance in Child Welfare and Healthcare service networks. Health Policy 2023, 127, 29–36. [Google Scholar] [CrossRef] [PubMed]
- Kiross, G.T.; Chojenta, C.; Barker, D.; Loxton, D. Optimum maternal healthcare service utilization and infant mortality in Ethiopia. BMC Pregnancy Childbirth 2021, 21, 390. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.; Ma, S.; Chen, S. Healthcare service configuration based on project scheduling. Adv. Eng. Inform. 2020, 43, 101039. [Google Scholar] [CrossRef]
- Tuli, S.; Basumatary, N.; Gill, S.S.; Kahani, M.; Arya, R.C.; Wander, G.S.; Buyya, R. HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments. Future Gener. Comput. Syst. 2020, 104, 187–200. [Google Scholar] [CrossRef] [Green Version]
- Zhang, C.; Cho, H.H.; Chen, C.Y. Emergency-level-based healthcare information offloading over fog network. Peer-Peer Netw. Appl. 2020, 13, 16–26. [Google Scholar] [CrossRef]
- Chen, Y. Convolutional Neural Network for Sentence Classification. Master’s Thesis, University of Waterloo, Waterloo, ON, Canada, 2015. [Google Scholar]
- Quy, V.K.; Hau, N.V.; Anh, D.V.; Ngoc, L.A. Smart healthcare IoT applications based on fog computing: Architecture, applicationsand challenges. Complex Intell. Syst. 2022, 8, 3805–3815. [Google Scholar] [CrossRef] [PubMed]
- Rehman, A.; Saba, T.; Haseeb, K.; Alam, T.; Lloret, J. Sustainability model for the internet of health things (IoHT) using reinforcement learning with Mobile edge secured services. Sustainability 2022, 14, 12185. [Google Scholar] [CrossRef]
- Rehman Khan, A.; Saba, T.; Sadad, T.; Hong, S.P. Cloud-based framework for COVID-19 detection through feature fusion with bootstrap aggregated extreme learning machine. Discret. Dyn. Nat. Soc. 2022, 2022, 3111200. [Google Scholar] [CrossRef]
- Du, G.; Tian, Y.; Ouyang, X. Multi-resources co-scheduling optimization for home healthcare services under the constraints of service time windows and green transportation. Appl. Soft Comput. 2022, 131, 109746. [Google Scholar] [CrossRef]
- Xu, L.Q.; Gao, H. A device-agnostic cloud-based platform enabling tiered service provisioning for sleep disorders diagnosis and management. In Encyclopedia of Sleep and Circadian Rhythms; Elsevier: Amsterdam, The Netherlands, 2022; pp. 88–99. [Google Scholar]
- Sabokbar, H.F.; Mohammadi, H.; Tahmasbi, S.; Rafii, Y.; Hosseini, A. Measuring spatial accessibility and equity to healthcare services using fuzzy inference system. Appl. Geogr. 2021, 136, 102584. [Google Scholar] [CrossRef]
- Abdelmoneem, R.M.; Benslimane, A.; Shaaban, E. Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures. Comput. Netw. 2020, 179, 107348. [Google Scholar] [CrossRef]
- Gouveia, L.; Paias, A.; Ponte, M. The travelling salesman problem with positional consistency constraints: An Application to healthcare services. Eur. J. Oper. Res. 2022, 308, 960–989. [Google Scholar] [CrossRef]
- Zgheib, R.; Kristiansen, S.; Conchon, E.; Plageman, T.; Goebel, V.; Bastide, R. A scalable semantic framework for IoT healthcare applications. J. Ambient. Intell. Humaniz. Comput. 2022, 13, 1735–1762. [Google Scholar] [CrossRef]
- Kumar, C.S.; Kumar, K.V. Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud. Intell. Autom. Soft Comput. 2023, 35, 2893–2907. [Google Scholar] [CrossRef]
- Rehman, A.; Saba, T.; Haseeb, K.; Singh, R.; Jeon, G. Smart health analysis system using regression analysis with iterative hashing for IoT communication networks. Comput. Electr. Eng. 2022, 104, 108456. [Google Scholar] [CrossRef]
- Waibel, S.; Williams, J.; Tuff, Y.; Shum, J.; Scarr, J.; Donnell, M. Development of the Tiers of Service framework to support system and operational planning for children’s healthcare services. BMC Health Serv. Res. 2021, 21, 693. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Wang, H.; Wang, N.; Zhang, T. Optimal scheduling in cloud healthcare system using Q-learning algorithm. Complex Intell. Syst. 2022, 8, 4603–4618. [Google Scholar] [CrossRef] [PubMed]
- Elshahed, E.M.; Abdelmoneem, R.M.; Shaaban, E.; Elzahed, H.A.; Al-Tabbakh, S.M. Prioritized scheduling technique for healthcare tasks in cloud computing. J. Supercomput. 2022, 79, 4895–4916. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kaliappan, V.K.; Gnanamurthy, S.; Yahya, A.; Samikannu, R.; Babar, M.; Qureshi, B.; Koubaa, A. Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities. Sustainability 2023, 15, 5457. https://doi.org/10.3390/su15065457
Kaliappan VK, Gnanamurthy S, Yahya A, Samikannu R, Babar M, Qureshi B, Koubaa A. Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities. Sustainability. 2023; 15(6):5457. https://doi.org/10.3390/su15065457
Chicago/Turabian StyleKaliappan, Vishnu Kumar, Sundharamurthy Gnanamurthy, Abid Yahya, Ravi Samikannu, Muhammad Babar, Basit Qureshi, and Anis Koubaa. 2023. "Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities" Sustainability 15, no. 6: 5457. https://doi.org/10.3390/su15065457