Predicting and Visualizing Covid-19 Identification by a Hybrid Machine Learning and Pre-trained Model

The emergence of the Omicron variant in Shanghai in 2022 has highlighted the need for effective diagnostic tools for COVID-19. Recent studies have indicated that cough sounds generate distinctive features capable of distinguishing infected individuals from healthy ones. This study proposes a hybrid approach that combines machine learning and pre-trained models, utilizing audio features such as Mel-frequency cepstral coefficients and waveforms as inputs to train both types of models for accurate identification of COVID-19 patients and healthy individuals. The study employs a dataset collected from hospitals in Shanghai, China, comprising 78 participants, including COVID-19 positive and negative individuals. The proposed method demonstrates superior performance in diagnosing COVID-19 compared to existing mainstream machine learning algorithms. Furthermore, decisive important audio features for the COVID-19 positive classifier are identified via SHAP values for feature importance. Overall, the proposed approach achieves excellent diagnostic accuracy for COVID-19, outperforming current mainstream machine learning methods. With its multiple strengths in performance, speed, and usability, this algorithm shows great promise in enabling large-scale screening and aiding the containment of future widespread infections.