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*.

本文提出了一种新的扩展的零阶自主学习多模型（ALMMo-0 *）神经模糊方法，以便通过声音对不同的心脏病进行分类。ALMMo-0 *是在最近推出的ALMMo-0的基础上构建的。本文通过添加预处理结构扩展了ALMMo-0，从而改善了所提出方法的性能。ALMMo-0 *作为由层次结构组成的学习引擎，由大量并行的0阶模糊规则组成，它们能够自适应并提供透明且易于理解的IF ... THEN表示。分析中考虑的心音记录来自世界各地的一些贡献者。从临床和非临床环境以及健康和病理患者收集数据。与主流机器学习方法不同，ALMMo-0 *能够从看不见的数据中学习。所提出的方法的主要目的是为心脏病诊断提供高度透明，可解释性和可解释性的高精度模型。实验表明，所提出的基于神经模糊的模型是这些挑战性分类任务的有效框架，在分类准确性方面超过了其最新的竞争对手。此外，ALMMo-0 *产生了透明的AnYa类型模糊规则，这些规则可以人工解释，并可以帮助专家提供更准确的诊断。通过将患者的样本与通过ALMMo-0 *从异常样本中识别出的原型进行比较，医生可以轻松地识别异常心音。