Prediction of Multi Drug Resistant Tuberculosis using Machine Learning Techniques
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2019 p1764-1771
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.
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