A Constructive Fuzzy Representation Model for Heart Data Classification

Abstract

The early detection of Heart Disease (HD) and the prediction of Heart Failure (HF) via telemonitoring and can contribute to the reduction of patients’ mortality and morbidity as well as to the reduction of respective treatment costs. In this study we propose a novel classification model based on fuzzy logic applied in the context of HD detection and HF prediction. The proposed model considers that data can be represented by fuzzy phrases constructed from fuzzy words, which are fuzzy sets derived from data. Advantages of this approach include the robustness of data classification, as well as an intuitive way for feature selection. The accuracy of the proposed model is investigated on real home telemonitoring data and a publicly available dataset from UCI.

Artificial Intelligence in Cardiology—A Narrative Review of Current Status

“…In this direction, promising perspectives arise with the introduction of fuzzy logic in signal interpretation, enabling an uncertainty-aware mapping of linguistic terms to numeric intervals. One successful paradigm combining ML and fuzzy logic for heart disease (HD) detection and heart failure (HF) prediction has been proposed by Vasilakakis et al [12]. The methodology of that study was extended and applied to develop a generic framework for the explainable classification of medical images that can be applied on any ML-based classification system [13].…”

Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps

“…However, a limitation of FCMs is that there is a need for human participation to determine the structure of the graph. Recent modified FCMs have been effectively applied to various medical problems, including Constructive Fuzzy Representation Model (CFRM) for heart disease classification [5], and Constructive FCM (CFCM) for depression severity estimation [4]. In this paper, we introduce an FCM model for interpretable emotion recognition, based on EEG signals.…”