2023
DOI: 10.17485/ijst/v16i16.60
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A Novel Two Tier Missing at Random Type Missing Data Imputation using Enhanced Linear Interpolation Technique on Internet of Medical Things

Abstract: Objectives: Data collection and distribution are essential components required for the victory of Internet of Medical Things (IoMT) system. Generally, missing data is the most recurrent problem that impacts an overall system performance. Methods: Missing data in IoMT systems can be caused by various factors, including faulty connections, external attacks, or sensing errors. Although missing data is ubiquitous in IoT, missing data imputation is hardly ever observed in an IoMT setting. As a result, doing analyti… Show more

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Cited by 2 publications
(5 citation statements)
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“…For MAR-type missing data, the Two Tier Missing Data Imputation (TT-MDI) technique is employed (6) . This technique discovers the imputation threshold using Manhattan distances between class centroids and related data instances in the first tier.…”
Section: Composite Dterm Model For Iomtmentioning
confidence: 99%
See 3 more Smart Citations
“…For MAR-type missing data, the Two Tier Missing Data Imputation (TT-MDI) technique is employed (6) . This technique discovers the imputation threshold using Manhattan distances between class centroids and related data instances in the first tier.…”
Section: Composite Dterm Model For Iomtmentioning
confidence: 99%
“…The TT-MDI (Two-Tier Missing Data Imputation) technique proposes a unique method for imputing missing data in a dataset (6) . The imputation is done in two tiers, where the first tier is used to identify the imputation threshold, and the second tier is used to impute the missing data using the threshold.…”
Section: Two Tier Missing Data Imputation (Tt-mdi) Techniquementioning
confidence: 99%
See 2 more Smart Citations
“…For missing at random (MAR) type missing data in IoMT, Iris Punitha et al [20] present a unique Two Tier Missing Data Imputation (TT-MDI) strategy based on an improved linear interpolation method. The cStick IoMT dataset from the Repository was used to evaluate the proposed TT-MDI technique for imputation of MAR missing data.…”
Section: Related Workmentioning
confidence: 99%