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Maji S has completed her PhD from Thapar University, India in the year 2013. She is presently working as the Assistant professor at DIT University, Dehradun, India. She has large number of publications that have been cited over 60 times, and his/her publication H-index is 5 and has been serving as a reviewer of reputed Journals.
Timely correct and rapid prediction of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is crucial for control and management of the TB. Various machine learning methods have been largely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. Even though, they are not properly validated in the large group of MTB samples across the globe in terms of resistance prediction and resistance marker identification. Our proposed Hybrid Machine technique named Hybrid-TBP can be used for the identification of Mycobacterium tuberculosis (MTB) resistance beside numerous existing TB drugs for the management and control of TB. In this hybrid machine learning tool initial data samples of MTB data samples is provided to the Principle Component Analysis (PCA) for feature selection and classification is performed for result generation by using Support Vector Machine (SVM) with polynomial kernel. This Hybrid-TBP can be utilized as a supporting software tools in the field of medical science for MTB resistance. As compared to other existing available software MTB prediction tools, this proposed technique gives better performance.