Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

COVID-19 Severity Analysis Using Improved Machine Learning Algorithm


Affiliations
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, India
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, India
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, India
 

The new pandemic produced by the COVID-19 virus has resulted in an overflow of medical treatment in clinical centers all over the world. The fast and exponential growth in the number of COVID-19-infected individuals has necessitated an effective and timely prediction of probable infections and their effects in order to reduce health-care quality overload. As a result, intelligent models are being developed and used to assist medical workers in making more accurate diagnoses concerning the health condition of COVID-19-infected individuals. The purpose of this research is to present an alternative algorithmic approach for predicting the health status of COVID-19 patients in Mexico. Different prediction models were assessed and compared, including Adaboost, gradient boosting machine, random forests, and light gradient boosting machine. Additionally, Grid search hyperparameter optimization is used to improve the algorithm's success rate. The optimal model feature analysis procedure is being carried out. The purpose of this study is to analyses features in terms of feature importance as indicated by SHapely adaptive exPlanations (SHAP) values in order to identify relevant predictive factors that can identify patients at high risk of mortality.

Keywords

Machine Learning, COVID-19, Hyperparameter Tuning, SHAP Analysis.
User
Notifications
Font Size

  • Albataineh, Zaid, Fatima Aldrweesh, and Mohammad A. Alzubaidi. 2023. “COVID-19 CT-Images Diagnosis and Severity Assessment Using Machine Learning Algorithm.” Cluster Computing 5(May 2022).

  • Attallah, Omneya. 2022. “An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.” Biosensors 12(5).

  • Ciotti, Marco et al. 2020. “The COVID-19 Pandemic.” Critical Reviews in Clinical Laboratory Sciences 0(0): 365–88. https://doi.org/10.1080/10408363.2020.1783198.

  • El-Kenawy, El Sayed M. et al. 2020. “Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images.” IEEE Access 8.

  • Gupta, Subhash Chandra, and Noopur Goel. 2023. “Predictive Modeling and Analytics for Diabetes Using Hyperparameter Tuned Machine Learning Techniques.” Procedia Computer Science 218(2022): 1257–69. https://doi.org/10.1016/j.procs.2023.01.104.

  • Kassania, Sara Hosseinzadeh et al. 2021. “Automatic Detection of Coronavirus Disease (COVID-19) in X-Ray and CT Images: A Machine Learning Based Approach.” Biocybernetics and Biomedical Engineering 41(3): 867–79.

  • Kini, Anita S. et al. 2022. “Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.” Contrast Media and Molecular Imaging 2022.

  • Li, Jifang, Genxu Li, Chen Hai, and Mengbo Guo. 2022. “Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm.” IEEE Access 10: 1522–32.

  • Madoery, Pablo G, Ramiro Detke, Lucas Blanco, and Sandro Comerci. 2020. “Since January 2020 Elsevier Has Created a COVID-19 Resource Centre with Free Information in English and Mandarin on the Novel Coronavirus COVID- 19 . The COVID-19 Resource Centre Is Hosted on Elsevier Connect , the Company ’ s Public News and Information .” (January).

  • Mansbridge, Nicola et al. 2018. “Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.” Sensors (Switzerland) 18(10): 1–16.

  • Ndwandwe, Duduzile, and Charles S. Wiysonge. 2021. “COVID-19 Vaccines.” Current Opinion in Immunology 71(Figure 1): 111–16. https://doi.org/10.1016/j.coi.2021.07.003.

  • Patel, Dhruv et al. 2021. “Machine Learning Based Predictors for COVID-19 Disease Severity.” Scientific Reports 11(1): 1–7. https://doi.org/10.1038/s41598-021-83967-7.

  • Rostami, Mehrdad, and Mourad Oussalah. 2022. “A Novel Explainable COVID-19 Diagnosis Method by Integration of Feature Selection with Random Forest.” Informatics in Medicine Unlocked 30(January): 100941. https://doi.org/10.1016/j.imu.2022.100941.

  • Shekar, B. H., and Guesh Dagnew. 2019. “Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data.” 2019 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019 (November): 1–8.

  • Siji George, C. G., and B. Sumathi. 2020. “Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction.” International Journal of Advanced Computer Science and Applications 11(9): 173–78.

  • Sreedharan, Radhika, and Archana Praveen Kumar. 2020. “Analysis and Prediction of Smart Data Using Machine Learning.” AIP Conference Proceedings 2240(Ml): 15–21.

  • Uddin, Shahadat, Arif Khan, Md Ekramul Hossain, and Mohammad Ali Moni. 2019. “Comparing Different Supervised Machine Learning Algorithms for Disease Prediction.” BMC Medical Informatics and Decision Making 19(1): 1–16.

  • Velavan, Thirumalaisamy P., and Christian G. Meyer. 2020. “The COVID-19 Epidemic.” Tropical Medicine and International Health 25(3): 278–80.

  • Wang, Wenyang, and Dongchu Sun. 2021. “The Improved AdaBoost Algorithms for Imbalanced Data Classification.” Information Sciences 563: 358–74. https://doi.org/10.1016/j.ins.2021.03.042.

  • https://www.kaggle.com/marianarfranklin/mexico-covid19-clinical-data/


Abstract Views: 219

PDF Views: 0




  • COVID-19 Severity Analysis Using Improved Machine Learning Algorithm

Abstract Views: 219  |  PDF Views: 0

Authors

Balraj Preet Kaur
Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, India
Harpreet Singh
Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, India
Rahul Hans
Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, India
Sanjeev Sharma
Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, India

Abstract


The new pandemic produced by the COVID-19 virus has resulted in an overflow of medical treatment in clinical centers all over the world. The fast and exponential growth in the number of COVID-19-infected individuals has necessitated an effective and timely prediction of probable infections and their effects in order to reduce health-care quality overload. As a result, intelligent models are being developed and used to assist medical workers in making more accurate diagnoses concerning the health condition of COVID-19-infected individuals. The purpose of this research is to present an alternative algorithmic approach for predicting the health status of COVID-19 patients in Mexico. Different prediction models were assessed and compared, including Adaboost, gradient boosting machine, random forests, and light gradient boosting machine. Additionally, Grid search hyperparameter optimization is used to improve the algorithm's success rate. The optimal model feature analysis procedure is being carried out. The purpose of this study is to analyses features in terms of feature importance as indicated by SHapely adaptive exPlanations (SHAP) values in order to identify relevant predictive factors that can identify patients at high risk of mortality.

Keywords


Machine Learning, COVID-19, Hyperparameter Tuning, SHAP Analysis.

References