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Updated: Sep 26, 2025

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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records.

Shivani Batra1, Rohan Khurana1, Mohammad Zubair Khan2

  • 1Department of Computer Science and Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad 201206, India.

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This study introduces an ensemble imputation model to accurately handle missing healthcare data. The novel approach outperforms traditional methods for reliable medical decision-making models.

Keywords:
ensemble learninghealth dataimputation methodsmissing valuesregression algorithms

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Area of Science:

  • Medical Informatics
  • Data Science
  • Machine Learning

Background:

  • Medical data frequently contains missing values, impacting the reliability of computer modeling for clinical decision support.
  • Missing data can occur in both training and testing datasets, complicating predictive accuracy.

Purpose of the Study:

  • To evaluate and propose an effective imputation strategy for handling missing values in healthcare datasets.
  • To develop an ensemble imputation model that selects the optimal imputation method based on data characteristics.

Main Methods:

  • An ensemble imputation model combining mean, k-nearest neighbor, and iterative imputation was developed.
  • The model dynamically selects imputation strategies based on attribute correlations within missing value features.
  • Performance was evaluated using eXtreme gradient boosting, random forest, and support vector regressors on real-world healthcare data.

Main Results:

  • The proposed ensemble imputation strategy demonstrated superior accuracy compared to standard imputation methods and data deletion.
  • Experiments included simulations with varying missing data frequencies to assess robustness.
  • The ensemble approach effectively addressed missing values in both training and testing datasets.

Conclusions:

  • The developed Ensemble Strategy for Missing Value (ESMV) provides an accurate and unbiased method for analyzing healthcare data with missing values.
  • This approach enhances the reliability of statistical modeling and medical decision-making.
  • The ESMV offers a significant improvement over existing missing data handling techniques.