Open AccessPosted Content
An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification.
TLDR
In this article, an ensemble-based multi-criteria decision-making (MCDM) method was proposed for selecting top performance machine learning technique(s) for COVID-19 cough classification.Abstract:
The objectives of this research are analysing the performance of the state-of-the-art machine learning techniques for classifying COVID-19 from cough sound and identifying the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (such as precision, sensitivity, specificity, AUC, accuracy, etc.) make it difficult to select the best performance model. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models.read more
Citations
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Posted ContentDOI
AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Results and Challenges of a Systematic Review of the Relevant Literature
Karl Kelley,Allison A. Sakara,Mona Kelley,S. Caitlin Kelley,Paul McLenaghan,Rodolfo Aldir,Morgan Cox,Nolan Donaldson,Adam Stogsdill,Simon Kotchou,Geri Sula,Maurice A. Ramirez +11 more
TL;DR: In this article, a comprehensive and systematic search of the relevant literature on signal data signature (SDS)-based artificial intelligence/machine learning (AI/ML) systems designed to aid in the diagnosis of COVID-19 illness was conducted.
Posted ContentDOI
AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Good Machine Learning Practice and Good Clinical Practices from Concept to Consumer for AI/ML Software Devices
TL;DR: In this paper, a comprehensive and systematic search of the relevant literature on signal data signature (SDS)-based artificial intelligence/machine learning (AI/ML) systems designed to aid in the diagnosis of COVID-19 illness was conducted.
References
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Book
Multiple Objective Decision Making ― Methods and Applications: A State-of-the-Art Survey
Ching-Lai Hwang,Kwangsun Yoon +1 more
TL;DR: On MADM Methods Classification.
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