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

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. https://www.indjst.org/