Article: Ensemble regression model-based missing not at random type missing data imputation on the internet of medical things Journal: International Journal of System of Systems Engineering (IJSSE) 2024 Vol.14 No.6 pp.583 - 604 Abstract: The Internet of Medical Things (IoMT) combines IoT and health sensing technologies, which allow for the early detection of various health issues. However, the data generated from IoMT devices may contain missing values or corrupted data, particularly when the missing data is of the missing not-at-random (MNAR) type. Existing solutions for imputing missing data in IoMT have limitations such as low accuracy and high computational cost. To overcome these limitations, this paper proposes an ensemble regression model (ERM) based on MNAR-type missing data progressive imputation (MDPI). The ERM-MDPI model combines three regression models, namely multilayer perceptron (MLP), support vector regression (SVR), and linear regression (LR), to improve the accuracy of imputed missing data in the cStick dataset. The experimental results demonstrate that the ERM-MDPI model-based cStick imputed dataset generated higher accuracy (93.6301%), precision (91.0385%), recall (87.0898%) and F-measure (89.0204%) than cStick missing dataset. Therefore, the proposed solution offers an efficient and accurate approach to impute MNAR-type missing data in IoMT, providing valuable insights for medical decision-making. Inderscience Publishers - linking academia, business and industry through research

Title: Ensemble regression model-based missing not at random type missing data imputation on the internet of medical things

Authors: P. Iris Punitha; J.G.R. Sathiaseelan

Addresses: Department of Computer Applications, Bishop Heber College (Autonomous), Tiruchirappalli – 620 017, Tamil Nadu, India ' Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli – 620 017, Tamil Nadu, India

Abstract: The Internet of Medical Things (IoMT) combines IoT and health sensing technologies, which allow for the early detection of various health issues. However, the data generated from IoMT devices may contain missing values or corrupted data, particularly when the missing data is of the missing not-at-random (MNAR) type. Existing solutions for imputing missing data in IoMT have limitations such as low accuracy and high computational cost. To overcome these limitations, this paper proposes an ensemble regression model (ERM) based on MNAR-type missing data progressive imputation (MDPI). The ERM-MDPI model combines three regression models, namely multilayer perceptron (MLP), support vector regression (SVR), and linear regression (LR), to improve the accuracy of imputed missing data in the cStick dataset. The experimental results demonstrate that the ERM-MDPI model-based cStick imputed dataset generated higher accuracy (93.6301%), precision (91.0385%), recall (87.0898%) and F-measure (89.0204%) than cStick missing dataset. Therefore, the proposed solution offers an efficient and accurate approach to impute MNAR-type missing data in IoMT, providing valuable insights for medical decision-making.

Keywords: IoMT; Internet of Medical Things; missing not-at-random (MNAR) data; ERM; ensemble regression model; progressive imputation; medical decision-making.

DOI: 10.1504/IJSSE.2024.143702

International Journal of System of Systems Engineering, 2024 Vol.14 No.6, pp.583 - 604

Received: 20 Mar 2023
Accepted: 09 May 2023
Published online: 06 Jan 2025 *

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Title: Ensemble regression model-based missing not at random type missing data imputation on the internet of medical things

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