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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

Sonia Goel1,#, Meena Tushir1, Jyoti Arora2, Tripti Sharma2, Deepali Gupta3, Ali Nauman4,#, Ghulam Muhammad5,*

1 Electrical and Electronics Engineering (EEE) Department, Maharaja Surajmal Institute of Technology, New-Delhi, 110058, India
2 Information Technology (IT) Department, Maharaja Surajmal Institute of Technology, New-Delhi, 110058, India
3 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India
4 Department of Computer Science and Engineering, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea
5 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11421, Saudi Arabia

* Corresponding Author: Ghulam Muhammad. Email: email
# Sonia Goel and Ali Nauman contributed equally to the article

Computers, Materials & Continua 2024, 81(2), 3125-3145. https://doi.org/10.32604/cmc.2024.054476

Abstract

In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm. The effectiveness of the proposed approach is evaluated using multiple performance metrics, including accuracy, precision, specificity, and sensitivity. The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

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Cite This Article

APA Style
Goel, S., Tushir, M., Arora, J., Sharma, T., Gupta, D. et al. (2024). An enhanced integrated method for healthcare data classification with incompleteness. Computers, Materials & Continua, 81(2), 3125-3145. https://doi.org/10.32604/cmc.2024.054476
Vancouver Style
Goel S, Tushir M, Arora J, Sharma T, Gupta D, Nauman A, et al. An enhanced integrated method for healthcare data classification with incompleteness. Comput Mater Contin. 2024;81(2):3125-3145 https://doi.org/10.32604/cmc.2024.054476
IEEE Style
S. Goel et al., “An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness,” Comput. Mater. Contin., vol. 81, no. 2, pp. 3125-3145, 2024. https://doi.org/10.32604/cmc.2024.054476



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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