Enhancing Patient Care with Machine Learning

Umamaheswara Reddy Kudumula

Abstract: This article delves into the transformative potential of machine learning ML within the healthcare sector, addressing persistent challenges like rising costs, discrepancies in care quality, and the urgency for accurate diagnoses. Through a detailed exploration, it demonstrates how ML enhances diagnostic precision, personalizes patient care, and streamlines efficiency, marking a significant shift towards proactive, data - informed healthcare. The document underscores the importance of overcoming obstacles such as data privacy concerns, infrastructure requirements, and the need for cross - disciplinary cooperation to fully harness MLs capabilities. Highlighting initiatives like the Biden Cancer Moonshot, it emphasizes a collaborative approach to integrating ML in healthcare, aiming to improve patient outcomes significantly. Furthermore, it outlines a multi - faceted solution, incorporating predictive analytics, personalized medicine, and improved operational efficiency, thereby advocating for a patient - centered, data - driven healthcare system that leverages ML for better health outcomes and quality of life.

Keywords: Machine Learning (ML), Healthcare Technology, Predictive Analytics, Personalized Medicine, Remote Patient Monitoring, Diagnostic Accuracy, Operational Efficiency, Data Privacy and Security, Interdisciplinary Collaboration, Center for Disease Control and Prevention (CDC), Artificial Intelligence (AI) in Healthcare

How to Cite?: Umamaheswara Reddy Kudumula, "Enhancing Patient Care with Machine Learning", Volume 13 Issue 3, March 2024, International Journal of Science and Research (IJSR), Pages: 1817-1819, https://www.ijsr.net/getabstract.php?paperid=SR24321093056, DOI: https://dx.doi.org/10.21275/SR24321093056