2021/10/01 by Nihad Karim Chowdhury, Chowdhury, Nihad Karim, Muhammad Ashad Kabir +3
Computer Science · Engineering · Medicine · #Anomaly Detection Techniques and Applications #Audio and Speech Processing (eess.AS) #COVID-19 diagnosis using AI #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Pneumonia and Respiratory Infections #Sound (cs.SD) #cs.LG #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.00508
21 pages, 6 figures
arxiv created 2021/10/01 · openalex publication_date 2021/10/01 · arxiv updated 2021/10/04 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28
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.