1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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 a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates.

          Related collections

          Most cited references48

          • Record: found
          • Abstract: not found
          • Article: not found

          Support-vector networks

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Induction of decision trees

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Statistical pattern recognition: a review

                Bookmark

                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                04 May 2022
                2022
                : 10
                : 858282
                Affiliations
                [1] 1Symbiosis Institute of Technology, Symbiosis International University , Pune, India
                [2] 2Department of Computer Science and Engineering, Siksha O Anusandhan Deemed to be University , Bhubaneshwar, India
                [3] 3Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University) , Pune, India
                [4] 4School of Computer Data and Mathematical Sciences, University of Western Sydney , Sydney, NSW, Australia
                Author notes

                Edited by: Durai Raj Vincent P. M., VIT University, India

                Reviewed by: Ananth J. P., Sri Krishna College of Engineering and Technology, India; Duraimurugan Samiayya, St. Joseph's College of Engineering, India

                This article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health

                Article
                10.3389/fpubh.2022.858282
                9114677
                f3accbbd-f48d-49c0-a769-af5bf0a7ebb9
                Copyright © 2022 Mishra, Shaw, Mishra, Patil, Kotecha, Kumar and Bajaj.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 January 2022
                : 15 March 2022
                Page count
                Figures: 15, Tables: 5, Equations: 18, References: 50, Pages: 17, Words: 6975
                Categories
                Public Health
                Original Research

                bit-fusion ensemble algorithm,classifier fusion,k-nearest neighbor,multi-layer perceptron,naïve bayesian classifier,support vector machine

                Comments

                Comment on this article