Adaptive Boosting Based Supervised Learning Approach for Covid-19 Prediction from Cough Audio Signals


  • Monelli Ayyavaraiah Research Scholar, Dayananda Sagar University-Bangalore, Assistant Professor, Department of Computer Science and Engineering, KKR & KSR Institute of Technology & Sciences (KITS), Vinjanampadu, Vatticherukuru Mandal, Guntur-522017.
  • Bondu Venkateswarlu Associate Professor, Dayananda Sagar University-Bangalore.


Random Forest, COVID-19, Cough Audio Signals, Machine Learning, Power spectrum, Optimal Features, Kruskal-Wallis, Adaptive Boosting Classifier


An increasing number of people have died as a result of the COVID-19 pandemic's second wave of breakout. As has been shown, several nations' healthcare systems are being destroyed by the second wave. Regional routine testing combined contact tracing can take the place of regional constraints in preventing the virus from propagating, and the "Track, Test, and Treat" programme has straightened the epidemic track in its early phases. Thus, to lower infection rates and minimise the negative effects on medical Machine learning along with feature engineering is a potential domain for developing Covid 19 positive as well as negative samples classification, a critical research objective in contemporary engineering. While there are effective machine learning-based methods to classify COVID-19 positive and negative samples like cough audio signals, detection accuracy with the highest possible sensitivity and specificity is still not scalable using the majority of contemporary methods. Typically, detection accuracy is proportional to the optimal features used to train the classifier. As a result, it is obvious that optimizing features for Covid 19 infection recognition from cough audio signals is a possible research objective. In support of this argument, this article suggested and described a novel technique “Adaptive Boosting based Supervised Learning (ABSL) Approach for Covid-19 Prediction from Cough Audio Signals”. The spectral features and Mel-frequency cepstral coefficients are used in the proposed model. The feature engineering has been done by the diversity assessment model “kruuskal-wallis test”. In addition, a novel binary classification strategy has been derived by using adaptive boosting strategy.   The experiments have been done on benchmark dataset to evaluate the proposed approach's performance against a comparable contemporary method Random forest classifier that trained by Mel-frequency cepstral coefficients (MFCCs). The experiments demonstrated that the suggested ABSL has the potential to escalate prediction accuracy with a low rate of false alarms.


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Kwok, Hang Fai. "The significance of advanced COVID-19 diagnostic testing in pandemic control measures." International Journal of Biological Sciences 18, no. 12 (2022): 4610.

Dagar, Eshwari SS, and Ramesh K. Sunkaria. "Analysis of Respiratory Signals in Spectral Domain for Detecting Respiratory Disorders with Emphasis on COVID-19." In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 2088-2093. IEEE, 2022.

Islam, Rumana, Esam Abdel-Raheem, and Mohammed Tarique. "A study of using cough audio signals and deep neural networks for the early detection of COVID-19." Biomedical Engineering Advances 3 (2022): 100025.

Lasker, Asifuzzaman, Sk Md Obaidullah, Chandan Chakraborty, and Kaushik Roy. "Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review." SN Computer Science 4, no. 1 (2022): 65.

Chatrzarrin, Hanieh, Amaya Arcelus, Rafik Goubran, and Frank Knoefel. "Feature extraction for the differentiation of dry and wet cough audio signals." In 2011 IEEE international symposium on medical measurements and applications, pp. 162-166. IEEE, 2011.

Jaiswal, S., & Gupta, P. (2023). Ensemble based Model for Diabetes Prediction and COVID-19 Mortality Risk Assessment in Diabetic Patients. International Journal of Computer Engineering In Research Trends, 10(3), 99-106.

Kosasih, Keegan, Udantha R. Abeyratne, Vinayak Swarnkar, and Rina Triasih. "Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis." IEEE Transactions on Biomedical Engineering 62, no. 4 (2014): 1185-1194.

Uysal, Sinem, Hüsamettin Uysal, Bülent Bolat, and Tülay Yıldırım. "Classification of normal and abnormal lung sounds using wavelet coefficients." In 2014 22nd Signal Processing and Communications Applications Conference (SIU), pp. 2138-2141. IEEE, 2014.

Monge-Álvarez, Jesús, Carlos Hoyos-Barceló, Luis Miguel San-José-Revuelta, and Pablo Casaseca-de-la-Higuera. "A machine hearing system for robust cough detection based on a high-level representation of band-specific audio features." IEEE Transactions on Biomedical Engineering 66, no. 8 (2018): 2319-2330.

Pramono, Renard Xaviero Adhi, Syed Anas Imtiaz, and Esther Rodriguez-Villegas. "Automatic identification of cough events from acoustic signals." In 2019 41st annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 217-220. IEEE, 2019.

Bhateja, Vikrant, Ahmad Taquee, and Dilip Kumar Sharma. "Pre-processing and classification of cough audio signals in noisy environment using SVM." In 2019 4th International Conference on Information Systems and Computer Networks (ISCON), pp. 822-826. IEEE, 2019.

Moradshahi, Payam. "Cough audio signal discrimination in noisy and reverberant environments using microphone arrays." PhD diss., Carleton University, 2012.

Taquee, Ahmad, Vikrant Bhateja, Adya Shankar, and Agam Srivastava. "Combination of wavelets and hard thresholding for analysis of cough audio signals." In 2018 Second World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 266-270. IEEE, 2018.

Victor Ikechukwu, A., Nivedha, K., Prakruthi, N. M., Fathima, F., Harini, R., & Shamitha, L. (2020). Diagnosis of Chronic Kidney Disease using Naïve Bayes algorithm Supported by Stage Prediction using eGFR. International Journal of Computer Engineering In Research Trends, 7(10), 6-12

Ramola, R. C. (2021). Challenges and Opportunities for Higher Education amid COVID-19 Pandemic. International Journal of Computer Engineering in Research Trends, 8(2), 29-32

.Suneel, Chenna Venkata, K. Prasanna, and M. Rudra Kumar. "Frequent data partitioning using parallel mining item sets and MapReduce." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2.4 (2017).

Rudra Kumar, M., Pathak, R., Gunjan, V.K. (2022). Machine Learning-Based Project Resource Allocation Fitment Analysis System (ML-PRAFS). In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore.

Ramana, Kadiyala, et al. "Leaf disease classification in smart agriculture using deep neural network architecture and IoT." Journal of Circuits, Systems and Computers 31.15 (2022): 2240004.

Vrindavanam, Jayavrinda, Raghunandan Srinath, Hari Haran Shankar, and Gaurav Nagesh. "Machine learning based COVID-19 cough classification models-a comparative analysis." In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 420-426. IEEE, 2021.

Rudra Kumar, M., Pathak, R., Gunjan, V.K. (2022). Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore.

Chalapathi, M. M., et al. "Ensemble Learning by High-Dimensional Acoustic Features for Emotion Recognition from Speech Audio Signal." Security and Communication Networks 2022 (2022).

Precision of 4-folds of leave-pair-out cross-validation of ABSL, and RF-MFCCs




How to Cite

Ayyavaraiah, M. ., & Venkateswarlu, B. . (2023). Adaptive Boosting Based Supervised Learning Approach for Covid-19 Prediction from Cough Audio Signals. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 38–51. Retrieved from



Research Article