Prediction of Multi Drug Resistant Tuberculosis using Machine Learning Techniques
R Lokeshkumar1, Jothi K R2, Anto S3, R Kiran kumar4, Hari Narayanan5
1R Lokeshkumar*, School of Computer Science & Engineering , Vellore Institute of Technology, Vellore, India.
2K R Jothi, School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, India.
3S Anto, School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, India.
4R Kiran kumar, School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, India.
5Hari Narayanan, School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 1764-1771 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2531129219/2019©BEIESP | DOI: 10.35940/ijeat.B2531.129219
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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: Mycobacterium Tuberculosis bacteria is the primary cause for Tuberculosis. TB is one of the main reasons of mortality around the world. Multi Drug Resistant Tuberculosis (MDR-TB) is a type of tuberculosis bacteria which are resistant to anti-TB drugs, drugs like isoniazid (INH) and rifampin (RMP). Different Machine learning approaches has been widely applied to predict MDR TB. Here, we review different Machine Learning Approaches to predict MDR-TB. Different feature estimation methods, execution of distinct machine learning models also have been explored. Additionally, the utilization of the distinctive machine learning system models for distinguishing the dis-functionalities of MDR-TB in the recent decades has been talked about.
Keywords: MDR-TB, Machine Learning, Genome Sequencing, isoniazid (INH) and rifampin (RMP).