Open AccessMachine Learning Medical Resource Allocation
Author(s) -
M. Sailaja,
Abdul Ahad,
K Sivaramakrishna,
A. B. R. M. Hussain
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2089/1/012082
Subject(s) - computer science , process (computing) , key (lock) , machine learning , artificial intelligence , order (exchange) , resource allocation , human resources , data science , resource (disambiguation) , computer security , computer network , management , finance , economics , operating system
In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes to assign health resources, and proposes a number of new ways to use these resources at the time of artificial intelligence in order to make human life part of this process and explore the good conditions which are shared by both the medical and computer industries.