Particle Swarm Optimization-Based Extreme Learning Machine for COVID-19 Detection

Musatafa Abbas Abbood Albadr; Sabrina Tiun; Masri Ayob; Fahad Taha Al-Dhief

doi:10.1007/s12559-022-10063-x

Particle Swarm Optimization-Based Extreme Learning Machine for COVID-19 Detection

Cognit Comput. 2022 Oct 12:1-16. doi: 10.1007/s12559-022-10063-x. Online ahead of print.

Authors

Musatafa Abbas Abbood Albadr¹, Sabrina Tiun¹, Masri Ayob¹, Fahad Taha Al-Dhief²

Affiliations

¹ CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor Malaysia.
² School of Electrical Engineering, Department of Communication Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia.

Abstract

COVID-19 (coronavirus disease 2019) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2. Recently, it has been demonstrated that the voice data of the respiratory system (i.e., speech, sneezing, coughing, and breathing) can be processed via machine learning (ML) algorithms to detect respiratory system diseases, including COVID-19. Consequently, many researchers have applied various ML algorithms to detect COVID-19 by using voice data from the respiratory system. However, most of the recent COVID-19 detection systems have worked on a limited dataset. In other words, the systems utilize cough and breath voices only and ignore the voices of the other respiratory system, such as speech and vowels. In addition, another issue that should be considered in COVID-19 detection systems is the classification accuracy of the algorithm. The particle swarm optimization-extreme learning machine (PSO-ELM) is an ML algorithm that can be considered an accurate and fast algorithm in the process of classification. Therefore, this study proposes a COVID-19 detection system by utilizing the PSO-ELM as a classifier and mel frequency cepstral coefficients (MFCCs) for feature extraction. In this study, respiratory system voice samples were taken from the Corona Hack Respiratory Sound Dataset (CHRSD). The proposed system involves thirteen different scenarios: breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowels. The experimental results demonstrated that the PSO-ELM was capable of attaining the highest accuracy, reaching 95.83%, 91.67%, 89.13%, 96.43%, 92.86%, 88.89%, 96.15%, 96.43%, 88.46%, 96.15%, 96.15%, 95.83%, and 82.89% for breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowel scenarios, respectively. The PSO-ELM is an efficient technique for the detection of COVID-19 utilizing voice data from the respiratory system.

Keywords: Mel frequency cepstral coefficients; Particle swarm optimization-extreme learning machine.

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