A Novel Two Tier Missing at Random Type Missing Data Imputation using Enhanced Linear Interpolation Technique on Internet of Medical Things

Punitha, P Iris; Sathiaseelan, J. G. R.

doi:10.17485/ijst/v16i16.60

Cited by 2 publications

(5 citation statements)

References 13 publications

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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%

“…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%

“…Enhanced Linear Interpolation (ELI) (6) is a method used to compute missing values in a dataset with class labels. It improves upon standard Linear Interpolation (LI) by considering the values of adjacent classes in addition to the values of the current class.…”

Section: Two Tier Missing Data Imputation (Tt-mdi) Techniquementioning

confidence: 99%

“…This paper proposes a new Composite DTERM imputation model for IoMT that integrates three stages: Dual Strategy-based Missing Data Imputation (DS-MDI), Two-Tier Missing Data Imputation (TT-MDI) (6) , and Ensemble Regression Model (ERM) based on MNAR-type missing data progressive imputation (MDPI).…”

Section: Introductionmentioning

confidence: 99%

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

Punitha,

Sathiaseelan

2023

IJST

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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. https://www.indjst.org/

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Section: Composite Dterm Model For Iomtmentioning

confidence: 99%

Section: Two Tier Missing Data Imputation (Tt-mdi) Techniquementioning

confidence: 99%

Section: Two Tier Missing Data Imputation (Tt-mdi) Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Punitha,

Sathiaseelan

2023

IJST

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“…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%

Analysis of Missing Health Care Data by Effective Adaptive DASO Based Naive Bayesian Model

M S,

et al. 2023

JMC

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Inevitably, researchers in the field of medicine must deal with the issue of missing data. Imputation is frequently employed as a solution to this issue. Unfortunately, the perfect would overfit the experiential data distribution due to the uncertainty introduced by imputation, which would have a negative effect on the replica's generalisation presentation. It is unclear how machine learning (ML) approaches are applied in medical research despite claims that they can work around lacking data. We hope to learn if and how machine learning prediction model research discuss how they deal with missing data. Information contained in EHRs is evaluated to ensure it is accurate and comprehensive. The missing information is imputed from the recognised EHR record. The Predictive Modelling approach is used for this, and the Naive Bayesian (NB) model is then used to assess the results in terms of performance metrics related to imputation. An adaptive optimisation technique, called the Adaptive Dolphin Atom Search Optimisation (Adaptive DASO) procedure, is used to teach the NB. The created Adaptive DASO method syndicates the DASO procedure with the adaptive idea. Dolphin Echolocation (DE) and Atom Search Optimisation (ASO) come together to form DASO. This indicator of performance metrics verifies imputation's fullness.

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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

Cited by 2 publications

References 13 publications

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

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

Analysis of Missing Health Care Data by Effective Adaptive DASO Based Naive Bayesian Model

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