Open AccessAnalyzing the Performance Factors of Machine Learning Algorithms for COVID'19 Data
Author(s) -
Rajkumar Venkatesan,
S. Nandhagopal,
A. Sabari
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7015.079920
Subject(s) - machine learning , artificial intelligence , computer science , unsupervised learning , semi supervised learning , online machine learning , instance based learning , process (computing) , algorithm , data set , globe , set (abstract data type) , big data , learning classifier system , data mining , programming language , operating system , medicine , ophthalmology
Machine learning is a branch of Artificial intelligence which provides algorithms that can learn from data and improve from experience, without human intervention. Now a day's many of the machine learning algorithms playing a vital role in data analytics. Such algorithms are possible to apply with the recent pandemic COVID situation across the globe. Machine learning algorithms are classified into 3 different groups based on the type of learning process, such as supervised learning, unsupervised learning, and reinforcement learning. By considering the medical observations on the COVID across the globe it has been discussed and concluded to analyze under the supervised learning process. The data set is acquired from the reliable source, it is processed and fed into the classification algorithms. Since learning behaviors are carried out by knowing the input data and expected output data. The data is labeled and has been classified based on labels. In the proposed work, three different algorithms are used to experiment with the COVID'19 dataset and compared for their efficiency and algorithm selection decision is made.