Rights statement: This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 94, 2020 DOI: 10.1016/j.asoc.2020.106449
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Autonomous Learning Multiple-Model Zero-Order Classifier for Heart Sound Classification
AU - Almeida Soares, Eduardo
AU - Angelov, Plamen Parvanov
AU - Gu, Xiaowei
N1 - This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 94, 2020 DOI: 10.1016/j.asoc.2020.106449
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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 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*.
AB - 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 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*.
KW - Autonomous Learning
KW - Data clouds
KW - Evolving fuzzy systems
KW - Heart sound classification
KW - Rule-based system
U2 - 10.1016/j.asoc.2020.106449
DO - 10.1016/j.asoc.2020.106449
M3 - Journal article
VL - 94
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
M1 - 106449
ER -