A Novel Composite DTERM-MDI Model for the Recovery of Missing Data in Internet of Medical Things

P Iris Punitha; J G R Sathiaseelan

doi:10.17485/IJST/v16i40.2064

Article

A Novel Composite DTERM-MDI Model for the Recovery of Missing Data in Internet of Medical Things

VIEWS 288
PDF 76

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i40.2064

Year: 2023, Volume: 16, Issue: 40, Pages: 3479-3490

Original Article

A Novel Composite DTERM-MDI Model for the Recovery of Missing Data in Internet of Medical Things

P Iris Punitha^1*, J G R Sathiaseelan²

¹Assistant Professor, Department of Computer Applications, Bishop Heber College (Affiliated to Bharathidasan University), Tiruchirappalli, 620017, Tamil Nadu, India
²Associate Professor, Department of Computer Science, Bishop Heber College (Affiliated to Bharathidasan University), Tiruchirappalli, 620017, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:14 August 2023, Accepted Date:15 September 2023, Published Date:27 October 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: The Internet of Things (IoT) relies on consistent data delivery, crucial for maintaining service quality. However, challenges like connection issues, external threats, and sensor malfunctions can lead to data insufficiency, affecting IoT applications. Addressing the problem of missing data in the vast streams of IoT-generated data is essential. This paper introduces a novel Composite DTERM Missing Data Imputation (MDI) model for the Internet of Medical Things (IoMT) aimed at robustly recovering missing data. Methods: The Composite DTERM-MDI model comprises three phases: Dual Strategy based Missing Data Imputation (DS-MDI), Two Tier Missing Data Imputation (TT-MDI), and Ensemble Regression Model (ERM) for progressive imputation of Missing Not at Random (MNAR) type data. The effectiveness of the Composite DTERM-MDI model is demonstrated using the cStick dataset, achieving significantly improved accuracy, precision, recall, and F-measure compared to the original dataset. Additionally, a comparison of the Composite DTERM-MDI model with two standard imputation techniques, MICE and SICE, is performed using the UCI car dataset. Findings: Experimental results showcase the superiority of the Composite DTERM-MDI model-based imputed cStick dataset, with accuracy at 99.12%, precision at 99.98%, recall at 98.53%, and F-measure at 98.89%, outperforming the original cStick dataset. The study highlights the Composite DTERM-MDI model's efficiency and accuracy in addressing missing data challenges in IoMT, which is vital for informed medical decision-making. Novelty: Furthermore, a comparison of the Composite DTERM-MDI model with MICE and SICE using the UCI car dataset evaluates accuracy and F-measure across four classification algorithms (PMM, POLYREG, CART, and LDA). The Composite DTERM-MDI model achieves accuracy rates of 91.08%, 81.58%, 97.74%, and 97.74%, along with F-measures of 84.97%, 89.58%, 98.71%, and 99.17% for PMM, POLYREG, CART, and LDA, respectively. This comparison demonstrates the model's performance against established imputation techniques in a different context.

Keywords: Internet of Medical Things (IoMT), Missing data imputation, Missing completely at random (MCAR), Missing at random (MAR), Missing not at random (MNAR), DTERM model

References

Dwivedi R, Mehrotra D, Chandra S. Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. Journal of Oral Biology and Craniofacial Research. 2022;12(2):302–318. Available from: https://doi.org/10.1016/j.jobcr.2021.11.010
Razdan S, Sharma S. Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies. IETE Technical Review. 2022;39(4):775–788. Available from: https://doi.org/10.1080/02564602.2021.1927863
Turabieh H, Mafarja M, Mirjalili S. Dynamic Adaptive Network-Based Fuzzy Inference System (D-ANFIS) for the Imputation of Missing Data for Internet of Medical Things Applications. IEEE Internet of Things Journal. 2019;6(6):9316–9325. Available from: https://doi.org/10.1109/JIOT.2019.2926321
Dekermanjian JP, Shaddox E, Nandy D, Ghosh D, Kechris K. Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics. BMC Bioinformatics. 2022;23(1):1–17. Available from: https://doi.org/10.1186/s12859-022-04659-1
Liu CHH, Tsai CFF, Sue KLL, Huang MWW. The Feature Selection Effect on Missing Value Imputation of Medical Datasets. Applied Sciences. 2020;10(7):1–12. Available from: https://doi.org/10.3390/app10072344
Punitha PI, Sathiaseelan JGR. A Novel Two Tier Missing at Random Type Missing Data Imputation using Enhanced Linear Interpolation Technique on Internet of Medical Things. Indian Journal of Science and Technology. 2023;16(16):1192–1204. Available from: https://doi.org/10.17485/IJST/v16i16.60
Barata AP, Takes FW, Herik HJVD, Veenman CJ. Imputation methods outperform missing-indicator for data missing completely at random. In: 2019 International Conference on Data Mining Workshops (ICDMW). (pp. 407-414) 2019.
Berrett TB, Samworth RJ. Optimal nonparametric testing of Missing Completely At Random, and its connections to compatibility. 2022. Available from: https://doi.org/10.48550/arXiv.2205.08627
Jin Q, Wang Z, Cai W, Zhang Y. Parameter identification for <scp>Wiener‐</scp>finite impulse response system with output data of missing completely at random mechanism and time delay. International Journal of Adaptive Control and Signal Processing. 2021;35(5):811–827. Available from: https://doi.org/10.1002/acs.3227
Ji F, Rabe-Hesketh S, Skrondal A. Diagnosing and Handling Common Violations of Missing at Random. Psychometrika. 2023;p. 1–21. Available from: https://doi.org/10.1007/s11336-022-09896-0
Garcia C, Leite D, Skrjanc I. Incremental Missing-Data Imputation for Evolving Fuzzy Granular Prediction. IEEE Transactions on Fuzzy Systems. 2020;28(10):2348–2362.
Ma C, Zhang C. Identifiable generative models for missing not at random data imputation. In: Advances in Neural Information Processing Systems (NeurIPS 2021). (Vol. 34, pp. 1-14) 2021.
Carreras G, Miccinesi G, Wilcock A, Preston N, Nieboer D, Deliens L, et al. Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study. BMC Medical Research Methodology. 2021;21(1):1–12. Available from: https://doi.org/10.1186/s12874-020-01180-y
Mera-Gaona M, Neumann U, Vargas-Canas R, López DM. Evaluating the impact of multivariate imputation by MICE in feature selection. PLOS ONE. 2021;16(7):1–28. Available from: https://doi.org/10.1371/journal.pone.0254720
Khan SI, Hoque ASML. SICE: an improved missing data imputation technique. Journal of Big Data. 2020;7(1):1–21. Available from: https://doi.org/10.1186/s40537-020-00313-w

Copyright

© 2023 Punitha & Sathiaseelan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)