Open Access

ARTICLE

An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

Sonia Goel^1,#, Meena Tushir¹, Jyoti Arora², Tripti Sharma², Deepali Gupta³, Ali Nauman^4,#, Ghulam Muhammad^5,*

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:
# 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

Received 29 May 2024; Accepted 15 October 2024; Issue published 18 November 2024

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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Keywords

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