부산대학교 도서관

The COVID-19 crisis has severely hampered the worldwide market, leading to several issues in the supply chain of several necessities, but a considerable increase in the healthcare sector for the pharmaceutical industry. Using machine learning, this research aims to comprehend and forecast pharmaceutical sector sales post-COVID-19. This paper analyzed the major non-communicable diseases and the pharmaceuticals used to treat them, discovered and determined the most significant factors, and utilized them to construct appropriate models for the study. An online survey was performed among Indian families using a structured questionnaire, including both open-ended and closed-ended questions on the family's health. Prior to and during the lockdown, information on non-communicable diseases and the usage of medications was gathered. Our results suggest that the unanticipated transformation in lifestyle has altered disease prevalence, which is a consideration for the pharmaceutical sector to address. And these models helped to figure out how disease levels were changing and how likely it was that the number of people with certain diseases would go up based on their symptoms. This gave a better idea of how to treat the patients.