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Related Experiment Video

Updated: May 26, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Cyclical hybrid imputation technique for missing values in data sets.

Kurban Kotan1, Serdar Kırışoğlu2

  • 1Department of Electrical Electronics and Computer Engineering, Graduate School of Education, Duzce University, 81620, Düzce, Turkey. kurbankotan@duzce.edu.tr.

Scientific Reports
|February 24, 2025
PubMed
Summary

Accurate imputation of missing data is crucial for reliable machine learning models, especially in healthcare. This study introduces a novel algorithm combining row-based and column-based imputation for superior accuracy in data preprocessing.

Keywords:
Artificial intelligenceDeep learningImputationMachine learningMissing values

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Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Missing data significantly impacts machine learning model accuracy and prediction performance.
  • Improper imputation methods, like mean or mode, can introduce errors and reduce reliability.
  • High rates of missing data in datasets compromise the integrity of analysis and modeling.

Purpose of the Study:

  • To address the critical issue of missing data imputation in datasets.
  • To develop and evaluate a novel algorithm for more effective missing value imputation.
  • To improve the performance of machine learning models by enhancing data preprocessing.

Main Methods:

  • Proposed a new imputation algorithm combining row-based and column-based techniques cyclically.
  • The algorithm considers individual missing values (column-based) and overall data structure (row-based).
  • Tested the algorithm on multiple datasets to assess its effectiveness compared to existing methods.

Main Results:

  • The proposed algorithm achieved 100% accuracy on certain datasets when combined with specific imputation techniques.
  • Demonstrated superior performance compared to traditional imputation methods.
  • Showcased the effectiveness of integrating row-based and column-based imputation strategies.

Conclusions:

  • Precise imputation of missing data leads to significant performance increases in machine learning models.
  • Artificial intelligence-driven imputation outperforms random or classical methods.
  • The novel cyclic imputation algorithm offers a highly accurate solution for missing data challenges.