Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems

Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems

Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into...

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Bibliographic Details
Main Authors: Sashikala Mishra, Kailash Shaw, Debahuti Mishra, Shruti Patil, Ketan Kotecha, Satish Kumar, Simi Bajaj
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-05-01
Series:Frontiers in Public Health
Subjects:
bit-fusion ensemble algorithm
classifier fusion
k-nearest neighbor
Multi-Layer Perceptron
Naïve Bayesian classifier
support vector machine
Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2022.858282/full

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https://www.frontiersin.org/articles/10.3389/fpubh.2022.858282/full

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