An Inclusive Examination and Comparison of Machine Learning Techniques in the Domain of Ebola Virus Disease
Nidhi Mehra1, Atika Bansal2, Divya Kapil3, Shivashish Dhondiyal4
1Nidhi Mehra, Department of Computing, Graphic Era Hill University, Dehradun (Uttarakhand), India.
2Atika Bansal, Department of Computing, Graphic Era Hill University, Dehradun (Uttarakhand), India.
3Divya Kapil, Department of Computing, Graphic Era Hill University, Dehradun (Uttarakhand), India.
4Shivashish Dhondiyal, Graphic Era Deemed to be University, Dehradun (Uttarakhand), India.
Manuscript received on 16 June 2019 | Revised Manuscript received on 23 June 2019 | Manuscript Published on 01 July 2020 | PP: 58-64 | Volume-8 Issue-2S12 September 2019 | Retrieval Number: B10100982S1219/2020©BEIESP | DOI: 10.35940/ijrte.B1010.0982S1219
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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: Diseases generated by viruses area unit transmitted, directly and indirectly will cause epidemics and pandemics. Despite the advances in medication and drugs , virus generated infectious diseases are one of the main reason behind death worldwide, particularly in low-income countries .Machine learning and computing are widely utilized in diagnose certain types of cancer from imaging knowledge/data and also in other clinical imaging data based diseases. This paper aims to investigate and compare machine learning classifiers for Ebola Virus Disease. The Kaggle data set for Ebola Virus diseases, containing 2486 instances, has been used as the database for the training and testing. For experimental analysis, we use Naïve Bayes, Random forest, and J 48 classification algorithms and show the results for TPR, precision FPR, F-measure, recall and ROC curve.
Keywords: Virus Generated Disease, Machine Learning, Classifiers.
Scope of the Article: Machine Learning