Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique

Zachariah Madaki, Nurettin Abacioglu, A. G. Usman*, Neda Taner, Ahmet O. Sehirli, S. I. Abba*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.

Original languageEnglish
Article number79
JournalLife
Volume13
Issue number1
DOIs
StatePublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • artificial intelligence
  • clinical variables
  • hepatitis C status
  • hybrid paradigms
  • machine learning

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • General Biochemistry, Genetics and Molecular Biology
  • Space and Planetary Science
  • Paleontology

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