A mathematical model based on modified ID3 algorithm for healthcare diagnostics model

Arun Agarwal, Khushboo Jain () and Rakesh Kumar Yadav
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Arun Agarwal: University of Delhi
Khushboo Jain: University of Petroleum and Energy Studies
Rakesh Kumar Yadav: Thapar Institute of Engineering and Technology

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 27, 2376-2386

Abstract: Abstract Data has become an asset in this digitization revolution, and the healthcare segment is the leading cause of this big data. Healthcare data analysis is an influential and powerful source for developing new visions to upsurge attentiveness to well-being. Data in the healthcare sector consists of various symptoms, treatments, disease information, patient information, and, lastly, tests to detect diseases. Healthcare information, along with machine learning (ML) Algorithms, supports the examination of big data to identify and discover the hidden patterns in any condition which can be used to predict any disease. This paper proposes a decision-support framework for any disease prediction in the healthcare sector. This work proposed an Improvised ID3 Algorithm (Modified ID3) which is based on a simple model of decision tree algorithm (ID3) to reduce time complexity and complex computation through the application of arithmetic operations for entropy computation and obtaining information. The Modified ID3 algorithm is implemented in python programming by using a reduced feature set of the Hepatitis C virus dataset (Hoffmann et al. in J Lab Precis Med 3:58, 2018) along with standard ML algorithms, such as ID3, support vector machine, random forest, and other recent states of artwork. The proficiency of this work and other ML algorithms are tested via a confusion matrix for various assessment parameters.

Keywords: Clinical data analytics; Confusion matrix; Data analytics; Decision tree; Modified ID3; Hepatitis C virus (HCV); Healthcare analytics (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-023-02086-w

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