journal-of-information-and-optimization-sciences Journal of Information and Optimization Sciences 2169-0103 0252-2667 Taru Publications 10.47974/JIOS-1782 A novel actuarial science approach for predicting infectious diseases using computed tomography Seetha J. Muma College of Business, 8350 N. Tamiami Trail Sarasota, University of South Florida, Florida, U.S.A. Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai, Tamil Nadu, 600123, India Unhelkar Bhuvan Muma College of Business, 8350 N. Tamiami Trail Sarasota, University of South Florida, Florida, U.S.A. Shankar S. Siva Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology, Moinabad, Telangana, 501504, India Chakrabarti Tulika Department of Chemistry, Sir Padampat Singhania University, Udaipur, Rajasthan, 313601, lndia Chakrabarti Prasun Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, 313601, lndia Sivaneasan B. Specialist Adult Educator Engineering, Electrical Power Engineering Programme, Singapore Institute of Technology, 828608, Singapore Margala Martin School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA- 70503, U.S.A. 45 8 2193 2202 18 12 2024

Amid the COVID-19 pandemic, early and accurate automatic detection is vital. This study presents the AVbADC Algorithm to separate lung areas impacted by COVID-19 in CT scans. Initial results show promising performance, though further improvements are needed. The refined AVbADC model emerges as a valuable tool for prompt COVID-19 case identification. Comparative analysis against existing methods provides insights into its efficacy.

 Infectious diseases Predictive modeling Corona-virus Computer tomography Lungs Tracking access open retracted no