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Dergiler / AURUM Mühendislik Sistemleri ve Mimarlık Dergisi / 2026 / Cilt: 7 Sayı: 1

A MISSING DATA IMPUTATION METHOD BASED ON GREY WOLF ALGORITHM FOR DIABETES DISEASE

Anas Ahmed(ALTINBAS UNIVERSITY)0000-0001-5324-3442
Timur İnan(ALTINBAS UNIVERSITY)0000-0002-6647-3025
Sayfa
55–72
Yayın Tarihi
2023-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In addition, the results indicated that IGW performed better than statistical imputation procedures such as removing samples with missing values, replacing them with zeros, mean, or random values.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The bulk of medical databases contain coverage gaps due in large part to the expensive expense of some tests or human error in documenting these tests.
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Temel Bulgular & Veri:Due to the absence of values for some features, the performance of the machine learning models is significantly impacted.
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Sonuç & Katkı:In addition, the results indicated that IGW performed better than statistical imputation procedures such as removing samples with missing values, replacing them with zeros, mean, or random values.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Missing Values · Grey Wolf Algorithm · Diabetes Disease. Classification

Abstract

The bulk of medical databases contain coverage gaps due in large part to the expensive expense of some tests or human error in documenting these tests. Due to the absence of values for some features, the performance of the machine learning models is significantly impacted. Consequently, a specific category of techniques is necessary for the aim of imputing missing data. In this study, the Grey Wolf Algorithm (GWA) is used to generate and impute the missing values in the Pima Indian Diabetes Disease (PIDD) dataset. The proposed method is known as the Pima Indian Diabetes Disease (PIDD) Algorithm (IGW). The obtained results demonstrated that the classification performance of three distinct classifiers, namely the Support Vector Machine (SVM), the K-Nearest Neighbor (KNN), and the Naive Bayesian Classifier (NBC), was enhanced in comparison to the dataset prior to the application of the proposed method. In addition, the results indicated that IGW performed better than statistical imputation procedures such as removing samples with missing values, replacing them with zeros, mean, or random values.

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Bu Makaleye Atıf Yap

Ahmed, A., İnan, T. (2023). A MISSING DATA IMPUTATION METHOD BASED ON GREY WOLF ALGORITHM FOR DIABETES DISEASE. AURUM Mühendislik Sistemleri ve Mimarlık Dergisi, Cilt: 7 Sayı: 1, 55–72. https://doi.org/10.53600/ajesa.1321182

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Kaynakça (16)

  1. Soofi, A. A., & Awan, A. (2017). Classification techniques in machine learning: applications and issues. J. Basic Appl. Sci, 13, 459-465.
  2. Tao, H., Awadh, S. M., Salih, S. Q., Shafik, S. S., & Yaseen, Z. M. (2022). Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction. Neural Computing and Applications, 34(1), 515-533.
  3. Tao, H., Salih, S., Oudah, A. Y., Abba, S. I., Ameen, A. M. S., Awadh, S. M.,. & Yaseen, Z. M. (2022). Development of new computational machine learning models for longitudinal dispersion coefficient determination: Case study of natural streams, United States. Environmental Science and Pollution Research, 29(24), 35841-35861.
  4. Sowmya, R., & Suneetha, K. R. (2017, January). Data mining with big data. In 2017 11th International Conference on Intelligent Systems and Control (ISCO) (pp. 246-250). IEEE.
  5. Donders, A. R. T., Van Der Heijden, G. J., Stijnen, T., & Moons, K. G. (2006). A gentle introduction to imputation of missing values. Journal of clinical epidemiology, 59(10), 1087-1091.
  6. García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2010). Pattern classification with missing data: a review. Neural Computing and Applications, 19, 263-282.
  7. Howell, D. C. (2007). The treatment of missing data. The Sage handbook of social science methodology, 208, 224.
  8. Pigott, T. D. “Handling Missing Data,” in Using Propensity Scores in Quasi-Experimental Designs, 1 Oliver’s Yard, 55 City Road London EC1Y 1SP: SAGE Publications, Ltd, 2009, pp. 245–271. doi: 10.4135/9781452270098.n11. Choudhury, S. J., & Pal, N. R. (2019). Imputation of missing data with neural networks for classification. Knowledge-Based Systems, 182, 104838.
  9. De Silva, H., & Perera, A. S. (2016, September). Missing data imputation using Evolutionary k-Nearest neighbor algorithm for gene expression data. In 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer) (pp. 141-146). IEEE.
  10. Alhroob, A., Alzyadat, W., Almukahel, I., & Altarawneh, H. (2020). Missing data prediction using correlation genetic algorithm and SVM approach. International Journal of Advanced Computer Science and Applications, 11(2).
  11. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  12. Long, W., Cai, S., Jiao, J., & Tang, M. (2020). An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization. Soft Computing, 24(2), 997-1026.
  13. Mohammadzadeh, A., Masdari, M., Gharehchopogh, F. S., & Jafarian, A. (2021). Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing. Evolutionary Intelligence, 14, 1997-2025.
  14. Tawhid, M. A., & Ali, A. F. (2017). A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memetic Computing, 9, 347-359.
  15. Zhang, X., Lin, Q., Mao, W., Liu, S., Dou, Z., & Liu, G. (2021). Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization. Applied Soft Computing, 101, 107061.
  16. Tao, H., Salih, S., Oudah, A. Y., Abba, S. I., Ameen, A. M. S., Awadh, S. M.,. & Yaseen, Z. M. (2022). Development of new computational machine learning models for longitudinal dispersion coefficient determination: Case study of natural streams, United States. Environmental Science and Pollution Research, 29(24), 35841-35861.