Analyzing the Performance Factors of Machine Learning Algorithms for COVID’19 Data
R. Venkatesan1, S.Nandhagopal2, A.Sabari3
1R.Venkatesan*, Assistant Professor, Department of Computer Science and Engineering & KSR Institute for Engineering & Technology, (T.N), India.
2S. Nandhagopal, Assistant Professor & Department of Information Technology & KSR Institute for Engineering & Technology, (T.N), India.
3A. Sabari, Professor & Head Department of Information Technology & K.S. Rangasamy College of Technology, (T.N), India.
Manuscript received on June 19, 2020. | Revised Manuscript received on June 22, 2020. | Manuscript published on July 10, 2020. | PP: 149-155 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7015079920 | DOI: 10.35940/ijitee.I7015.079920
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Machine learning, Data analysis, COVID, Algorithm analysis, kNN, SVM, Random Forest, Supervised Learning.
Scope of the Article: Machine Learning