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Autonomous Learning Multiple-Model zero-order classifier for heart sound classification

Soares, Eduardo, Angelov, Plamen, Gu, Xiaowei (2020) Autonomous Learning Multiple-Model zero-order classifier for heart sound classification. Applied Soft Computing, 94 . p. 106449. ISSN 1568-4946. (doi:10.1016/j.asoc.2020.106449) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90404)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.asoc.2020.106449

Abstract

This paper proposes a new extended zero-order Autonomous Learning Multiple-Model (ALMMo-0*) neuro-fuzzy approach in order to classify different heart disorders through sounds. ALMMo-0* is build upon the recently introduced ALMMo-0. In this paper ALMMo-0 is extended by adding a pre-processing structure which improves the performance of the proposed method. ALMMo-0* has as a learning engine composed of hierarchical a massively parallel set of 0-order fuzzy rules, which are able to self-adapt and provide transparent and human understandable IF ... THEN representation. The heart sound recordings considered in the analysis were sourced from several contributors around the world. Data were collected from both clinical and nonclinical environment, and from healthy and pathological patients. Differently from mainstream machine learning approaches, ALMMo-0* is able to learn from unseen data. The main goal of the proposed method is to provide highly accurate models with high transparency, interpretability, and explainability for heart disorder diagnosis. Experiments demonstrated that the proposed neuro-fuzzy-based modeling is an efficient framework for these challenging classification tasks surpassing its state-of-the-art competitors in terms of classification accuracy. Additionally, ALMMo-0* produced transparent AnYa type fuzzy rules, which are human interpretable, and may help specialists to provide more accurate diagnosis. Medical doctors can easily identify abnormal heart sounds by comparing a patient’s sample with the identified prototypes from abnormal samples by ALMMo-0*.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2020.106449
Uncontrolled keywords: Autonomous Learning; Data clouds; Evolving fuzzy systems; Heart sound classification; Rule-based system
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 10:14 UTC
Last Modified: 29 Sep 2021 11:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90404 (The current URI for this page, for reference purposes)
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