Research Article
Prediction of Tuberculosis disease using Data Mining Algorithms
@INPROCEEDINGS{10.4108/eai.27-2-2020.2303126, author={Saksham Maggo and Anmol Gupta and Sahil Jamwal and Prachi Setia and Sonia Rathee}, title={Prediction of Tuberculosis disease using Data Mining Algorithms}, proceedings={Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India}, publisher={EAI}, proceedings_a={ICIDSSD}, year={2021}, month={3}, keywords={data mining tuberculosis pls-da mlr k-nn mycobacterium k-means apriori lda}, doi={10.4108/eai.27-2-2020.2303126} }- Saksham Maggo
Anmol Gupta
Sahil Jamwal
Prachi Setia
Sonia Rathee
Year: 2021
Prediction of Tuberculosis disease using Data Mining Algorithms
ICIDSSD
EAI
DOI: 10.4108/eai.27-2-2020.2303126
Abstract
The expectation and finding of Tuberculosis survivability have been a difficult research issue. Since the early dates of the related research, much headway has been recorded in a few related fields. For example, the biomedical advancements have better logical prognostic elements are being estimated and recorded; the low-cost computer technology and the hardware gives better quality information and the data which is gathered has been analyzed by using the different analytics methods. Tuberculosis is one of the main illnesses for all individuals in developed nations including India. It is the most widely recognized reason for death in individual. The high occurrence of Tuberculosis in all individuals has expanded essentially in the most recent years. In this paper we have talked about different data mining approaches that have been used for Tuberculosis diagnosis .and anticipation. Here, we exploited those techniques which gives better prediction results of the Tuberculosis survivability.