The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Implementing Innovative Strategies for Advancing Healthcare Outcomes by Leveraging Machine Learning Technologies and Data-Driven Approaches
|
|
Author(s): R. Senthilkumar (Shree Venkateshwara Hi-Tech Engineering College, India), S. Prakasam (Shree Venkateshwara Hi-Tech Engineering College, India), P. M. Manochitra (Shree Venkateshwara Hi-Tech Engineering College, India), R. Kavishree (Muthayammal Engineering College, India)and K. Farhanath (OCS Infotech LLC, Oman)
Copyright: 2024
Pages: 15
Source title:
Cross-Industry AI Applications
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5951-8.ch016
Purchase
|
Abstract
The healthcare industry is undergoing a transformation, with data-driven techniques and machine learning (ML) technology playing critical roles in the process of altering patient care and management. One of the goals of this research is to investigate the implementation of innovative tactics that make use of machine learning technology in order to improve healthcare outcomes. It is possible for machine learning algorithms to discover patterns and insights that improve diagnostic accuracy, forecast patient trajectories, and optimise treatment regimens by utilising the massive datasets that are now prevalent in the healthcare industry. The study investigates a variety of machine learning models and their application in a wide range of healthcare domains. Additionally, it assesses the influence that these models have on patient outcomes and the delivery of healthcare. Through a strategic combination of theoretical frameworks and practical models, the research outlines a path that can be taken to achieve improved, individualised, and egalitarian healthcare for all individuals.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|