Work place: IERDS, MaddurNagar, Kurnool, Andhra Pradesh, India

E-mail: krlakshmi_cse@yahoo.com


Research Interests: Data Mining, Data Structures and Algorithms


K.R.Lakshmi: completed Master degree in Computer Applications in 2010 from Sri Krishnadevaraya University, Anantapur, Andhra Pradesh, India. She is a Director, IERDS, MaddurNagar, Kurnool, Andhra Pradesh, India. Her teaching and research areas interests Data mining techniques. She has published 5 articles in international well reputed journals.

Author Articles
Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

By K.R.Lakshmi M.Veera Krishna S.Prem Kumar

DOI: https://doi.org/10.5815/ijmecs.2013.08.02, Pub. Date: 8 Aug. 2013

The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability.

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