ML- Based Computational Methods to Predict, and Diagnosis of Pandemic Diseases

• Author(s): Kandala Kalyana Srinivas ; Mohamad Aziz Athani ; D. Karthikeya Srivatsa ; P. Sathwik
• Paper ID: 1706022
• Page: 483-492
• Published Date: 15-07-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 12 June-2024

Abstract

Machine learning (ML) has emerged as a transformative tool in predicting and diagnosing pandemic diseases, offering unprecedented accuracy and speed. This research paper delves into the development and application of ML-based computational methods in the context of pandemic disease management. By leveraging large-scale datasets, including genomic, epidemiological, and clinical data, ML algorithms can identify patterns and make predictions about disease spread, mutation, and patient outcomes. The paper discusses various ML techniques such as supervised learning, unsupervised learning, and reinforcement learning, and their specific applications in pandemic scenarios. Key case studies, including the use of ML in the COVID-19 pandemic, demonstrate how these methods have improved early detection, outbreak forecasting, and patient triage. Additionally, the paper explores the integration of ML with other technologies such as artificial intelligence (AI), big data analytics, and Internet of Things (IoT) devices to enhance real-time surveillance and response. Ethical considerations, data privacy issues, and the need for robust and unbiased algorithms are also addressed to ensure the reliability and equity of ML applications in healthcare. The findings underscore the potential of ML to revolutionize pandemic preparedness and response, providing healthcare professionals and policymakers with powerful tools to mitigate the impact of future pandemics. By advancing our understanding of how ML can be effectively applied to predict and diagnose pandemic diseases, this research contributes to the ongoing efforts to enhance global health security and resilience.

Citations

IRE Journals:
Kandala Kalyana Srinivas , Mohamad Aziz Athani , D. Karthikeya Srivatsa , P. Sathwik "ML- Based Computational Methods to Predict, and Diagnosis of Pandemic Diseases" Iconic Research And Engineering Journals Volume 7 Issue 12 2024 Page 483-492

IEEE:
Kandala Kalyana Srinivas , Mohamad Aziz Athani , D. Karthikeya Srivatsa , P. Sathwik "ML- Based Computational Methods to Predict, and Diagnosis of Pandemic Diseases" Iconic Research And Engineering Journals, 7(12)