Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Machine learning algorithms for smart and intelligent healthcare system in Society 5.0

Published: 29 December 2022 Publication History
  • Get Citation Alerts
  • Abstract

    The pandemic has shown us that it is quite important to keep track record our health digitally. And at the same time, it also showed us the great potential of Instruments like wearable observing gadgets, video conferences, and even talk bots driven by artificial intelligence (AI) can provide good care from remotely. Real time data collected from different health care devices of cases across globe played an important role in combatting the virus and also help in tracking its progress. The evolution of biomedical imaging techniques, incorporated sensors, and machine learning (ML) in recent years has led in various health benefits. Medical care and biomedical sciences have become information science fields, with a solid requirement for refined information mining techniques to remove the information from the accessible data. Biomedical information contains a few difficulties in information investigation, including high dimensionality, class irregularity, and low quantities of tests. AI is a subfield of AI and computer science which centric the utilization of information and calculations to impersonate the way that people learn, steadily further developing its accuracy. ML is an essential element of the rapidly growing area of information science. Calculations are created using measurable procedures to make characterizations or forecasts, exposing vital experiences inside information mining operations. In this chapter, we explain and compare the different algorithms of ML which could be helpful in detecting different disease at earlier stage. We summarize the algorithms and different steps involved in ML to extract information for betterment of the society which is already exposed to the world of data.

    References

    [1]
    Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak. 2020;20:16.
    [2]
    Mishra V, Singh Y, Kumar Rath S. Breast cancer detection from thermograms using feature extraction and machine learning techniques. Proceedings of the IEEE 5th International Conference for Convergence in Technology, Bombay, India; 2019.
    [3]
    Tautan A‐M, Ionescu B, Santarnecchi E., et al. Artificial intelligence in neurodegenerative diseases: a review of available tools with a focus on machine learning techniques. Artif Intell Med. 2021;117:102081.
    [4]
    Nielsen MA. Neural Networks and Deep Learning. Vol 25. Determination Press; 2015.
    [5]
    Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care. IEEE Computer Society Press; 1988:261‐265.
    [6]
    Schuld M, Sinayskiy I, Petruccione F. An introduction to quantum machine learning. arXiv:1409.3097v1.
    [7]
    Lloyd S, Mohseni M, Rebentrost P. Quantum algorithms for supervised and unsupervised machine learning. arXiv:1307.0411.
    [8]
    Lee MT, Suh I. Understanding the effects of environment, social, and governance conduct on financial performance: arguments for a process and integrated modelling approach. Sustain Technol Entrepreneur. 2022;1(1):100004. doi:10.1016/j.stae.2022.100004
    [9]
    Sarrab M, Alshohoumi F. Assisted‐fog‐based framework for IoT‐based healthcare data preservation. Int J Cloud Appl Comput. 2021;11:1‐16. doi:10.4018/IJCAC.2021040101
    [10]
    Guaita Martínez JM, Carracedo P, Gorgues Comas D, Siemens CH. An analysis of the blockchain and COVID‐19 research landscape using a bibliometric study. Sustain Technol Entrepreneur. 2022;1(1):100006. doi:10.1016/j.stae.2022.100006
    [11]
    Kumar N, Narayan Das N, Gupta D, Gupta K, Bindra1 J. Efficient automated disease diagnosis using machine learning models. J Healthcare Eng. 2021;2021:Article ID 9983652, 13.
    [12]
    Singh G, Malhotra M, Sharma A. An adaptive mechanism for virtual machine migration in the cloud environment. Int J Cloud Appl Comput. 2022;12:1‐10. doi:10.4018/IJCAC.297095
    [14]
    Pan X, Yamaguchi S, Kageyama T, Kamilin MHB. Machine‐learning‐based white‐hat worm launcher in botnet defense system. Int J Softw Sci Comput Intell. 2022;14:1‐14. doi:10.4018/IJSSCI.291713
    [15]
    Mishra A, Gupta N. Supervised machine learning algorithms based on classification for detection of distributed denial of service attacks in SDN‐enabled cloud computing. In: Agrawal DP, Nedjah N, Gupta BB, Martinez Perez G, eds. Cyber Security, Privacy and Networking. Springer; 2022:165‐174.
    [16]
    Onyebuchi A, Matthew UO, Kazaure JS, et al. Business demand for a cloud enterprise data warehouse in electronic healthcare computing: issues and developments in E‐healthcare cloud computing. Int J Cloud Appl Comput. 2022;12:1‐22. doi:10.4018/IJCAC.297098
    [17]
    Yu HQ, Reiff‐Marganiec S. Learning disease causality knowledge from the web of health data. Int J Semant Web Inf Syst. 2022;18:1‐19. doi:10.4018/IJSWIS.297145
    [18]
    Hammad M, Abd El‐Latif AA, Hussain A, Abd El‐Samie FE, Gupta BB. Deep learning models for arrhythmia detection in iot healthcare applications. Comput Electr Eng. 2022;100:108011.
    [19]
    Farhat H, Sakr GE, Kilany R. Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID‐19. Mach Vis Appl. 2020;31(6):53. doi:10.1007/s00138-020-01101-5
    [20]
    Sarivougioukas J, Vagelatos A. Fused contextual data with threading technology to accelerate processing in home UbiHealth. Int J Softw Sci Comput Intell. 2022;14:1‐14. doi:10.4018/IJSSCI.285590
    [21]
    Bennett KP, Mangasarian OL. Robust linear programming discrimination of two linearly inseparable sets. Optim Methods Softw. 1992;1:23‐34.
    [22]
    Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc.; 1988.
    [23]
    Lu S, Braunstein SL. Quantum decision tree classifier. Quantum Inf Process. 2014;13:757‐770. doi:10.1007/s11128-013-0687-5
    [24]
    John N, Sam S. Provably secure data sharing approach for personal health records in cloud storage using session password, data access key, and circular interpolation. Int J Semant Web Inf Syst. 2021;17:76‐98. doi:10.4018/IJSWIS.2021100105
    [25]
    Hatem JA, Dhaini AR, Elbassuoni S. Deep learning‐based dynamic bandwidth allocation for future optical access networks. IEEE Access. 2019;7:97307‐97318. doi:10.1109/ACCESS.2019.2929480
    [26]
    Salman O, Elhajj IH, Kayssi A, Chehab A. Mutated traffic detection and recovery: an adversarial generative deep learning approach. Ann Telecommun. 2022;77(5‐6):395‐406. doi:10.1007/s12243-022-00909-8
    [27]
    Monterola C, Saloma C. Solving the nonlinear Schrodinger equation with an unsupervised neural network. Opts Exp. 2001;9:72.
    [28]
    Manasrah AM, Aldomi A, Gupta BB. An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Clust Comput. 2019;22(1):1639‐1653
    [29]
    Salman O, Elhajj IH, Chehab A, Kayssi A. A machine learning based framework for IoT device identification and abnormal traffic detection. Trans Emerg Telecommun Technol. 2022;33(3):e3743. doi:10.1002/ett.3743
    [30]
    Mishra A, Gupta BB, Joshi RC. A comparative study of distributed denial of service attacks, intrusion tolerance and mitigation techniques. 2011 European Intelligence and Security Informatics Conference. IEEE; 2011:286‐289.
    [31]
    Abdulrahman S, Tout H, Ould‐Slimane H, Mourad A, Talhi C, Guizani M. A survey on federated learning: the journey from centralized to distributed on‐site learning and beyond. IEEE Internet Things J. 2021;8(7):5476‐5497. doi:10.1109/JIOT.2020.3030072
    [32]
    Cvitić I, Peraković D, Periša M, Gupta B. Ensemble machine learning approach for classification of IoT devices in smart home. Int J Mach Learn Cybern. 2021;12(11):3179‐3202.
    [33]
    Carleo G, Troyer M. Solving the quantum many‐body problem with artificial neural networks. Science. 2017;355:602.
    [34]
    Jia ZA, Yi B, Zhai R, Wu Y‐C, Guo G‐C, Guo G‐P. Quantum neural network states: a brief review of methods and applications. Adv Quantum Tech. 2019:1800077. doi:10.1002/qute.201800077
    [35]
    Gupta BB, Misra M, Joshi RC. An ISP level solution to combat DDoS attacks using combined statistical based approach. arXiv preprint arXiv:1203.2400. 2012.
    [36]
    Ibrahim SAH, Nassar M. On the security of deep learning novelty detection. Expert Syst Appl. 2022;207. doi:10.1016/j.eswa.2022.117922
    [37]
    Farhi E, Neven H. Classification with quantum neural networks on near term processors. arXiv:1802.06002v2.
    [38]
    Ghandour AJ, Hammoud H, Al‐Hajj S. Analyzing factors associated with fatal road crashes: A machine learning approach. Int J Environ Res Public Health. 2020;17(11):4111. doi:10.3390/ijerph17114111
    [39]
    Caetano C, Reis Jr. J, Amorim J, Lemes MR, Pino Jr. AD. Using neural networks to solve nonlinear differential equations in atomic and molecular physics. Int J Quantum Chem. 2011;111:2732.
    [40]
    Rebetrost P, Bromley TR, Weedbrook C, Lloyd S. Quantum hopfield neural network. Phy Rev A. 2018;98:042308.
    [41]
    Bardus M, Al Daccache M, Maalouf N, Al Sarih R, Elhajj IH. Data management and privacy policy of COVID‐19 contact‐tracing apps: systematic review and content analysis. JMIR MHealth and UHealth. 2022;10(7). doi:10.2196/35195
    [42]
    Negi P, Mishra A, Gupta BB. Enhanced CBF packet filtering method to detect DDoS attack in cloud computing environment. arXiv preprint arXiv:1304.7073. 2013.
    [43]
    Almomani A, Al‐Nawasrah A, Alomoush W, Al‐Abweh M, Alrosan A, Gupta BB. Information management and IoT technology for safety and security of smart home and farm systems. J Glob Inf Manag. 2021;29(6):1‐23.
    [44]
    Abbas N, Sharafeddine S, Mourad A, Abou‐Rjeily C, Fawaz W. Joint computing, communication and cost‐aware task offloading in D2D‐enabled Het‐MEC. Comput Netw. 2022;209:108900. doi:10.1016/j.comnet.2022.108900
    [45]
    Salloum G, Tekli J. Automated and personalized meal plan generation and relevance scoring using a multi‐factor adaptation of the transportation problem. Soft Comput. 2022;26(5):2561‐2585. doi:10.1007/s00500-021-06400-1
    [46]
    Hammoud A, Otrok H, Mourad A, Dziong Z. On demand fog federations for horizontal federated learning in IoV. IEEE Trans Netw Service Manag. 2022;19(3):3062‐3075. doi:10.1109/TNSM.2022.3172370
    [47]
    Salman O, Elhajj IH, Kayssi A, Chehab A. Data representation for CNN based Internet traffic classification: a comparative study. Multimed Tools Appl. 2021; 80(11):16951‐16977. doi:10.1007/s11042-020-09459-4

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image International Journal of Intelligent Systems
    International Journal of Intelligent Systems  Volume 37, Issue 12
    December 2022
    2488 pages
    ISSN:0884-8173
    DOI:10.1002/int.v37.12
    Issue’s Table of Contents

    Publisher

    John Wiley and Sons Ltd.

    United Kingdom

    Publication History

    Published: 29 December 2022

    Author Tags

    1. machine learning
    2. smart health care systems
    3. Society 5.0

    Qualifiers

    • Research-article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 0
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 11 Aug 2024

    Other Metrics

    Citations

    View Options

    View options

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media