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

Full Text (PDF, 352KB), PP.8-17

Views: 0 Downloads: 0


K.R.Lakshmi 1,* M.Veera Krishna 2 S.Prem Kumar 3

1. IERDS, MaddurNagar, Kurnool, Andhra Pradesh, India

2. Department of Mathematics, Rayalaseema University, Kurnool, Andhra Pradesh, India

3. Department of CSE&IT, G.Pullaiah college of Engineering & Technology, Nandikotkur Road, Kurnool, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2013.08.02

Received: 12 May 2013 / Revised: 6 Jun. 2013 / Accepted: 1 Jul. 2013 / Published: 8 Aug. 2013

Index Terms

SVM, C4.5, k-NN, PLS-DA, Data mining techniques, Tuberculosis and Specificity


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.

Cite This Paper

K.R.Lakshmi, M.Veera Krishna, S.Prem Kumar, "Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.8, pp.8-17, 2013. DOI:10.5815/ijmecs.2013.08.02


[1]Tamer Uçar a, Adem Karahocaa, “Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches”, CiteseerX, Vol. 3, No. 0, 2011.
[2]Asha.T, S. Natarajan and K.N.B. Murthy, “Diagnosis of Tuberculosis using Ensemble methods”, IEEE, 2010, 978-1-4244-5539-3/10.
[3]Minou Rabiei, Ritu Gupta “Excess Water Production Diagnosis in Oil Fields using Ensemble Classifiers” IEEE, 2009.
[4]Hongqi Li, Haifeng Guo, Haimin Guo and Zhaoxu Meng “Data Mining Techniques for Complex Formation Evaluation in Petroleum Exploration and Production: A Comparison of Feature Selection and Classification Methods” in proceedings of 2008 IEEE Pacific-Asia, Workshop on Computational Intelligence and Industrial Application, Vol. 1, pp. 37-43.
[5]Zhenzheng Ouyang, Min Zhou, Tao Wang and Quanyuan Wu, “Mining Concept-Drifting and Noisy Data Streams using Ensemble Classifiers”, International Conference on Artificial Intelligence and Computational Intelligence, Nov. 2009, pp. 360-364.
[6]Orhan Er, Feyzullah Temurtas and A.C. Tantrikulu, “Tuberculosis disease diagnosis using Artificial Neural networks ”, Journal of Medical Systems, Springer, 2008, DOI 10.1007/s10916-008-9241-x online.
[7]M. Sebban, I. Mokrousov, N. Rastogi and C. Sola “A data-mining approach to spacer oligo nucleotide typing of Mycobacterium tuberculosis” Bioinformatics, oxford university press, Vol. 18, Issue 2, 2002, pp. 235-243.
[8]Nicandro Cruz-Ram_rez, Hector-Gabriel Acosta-Mesa , Humberto Carrillo-Calvet , Roc_o-Erandi Barrientos-Mart_nez, “Discovering interobserver variability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks” Applied Soft Computing, Elsevier, Vol. 9, Issue 4, September 2009, pp. 1331-1342.
[9]Seppo Puuronen, Vagan Terziyan and Alexander Logvinovsky, “Mining Several Data Bases With an Ensemble of Classifiers” in Proc. 10th International Conference on Database and Expert Systems Applications, Vol. 1, No. 7, 1999, pp. 882-891.
[10]Tzung-I Tang,Gang Zheng ,Yalou Huang ,Guangfu Shu, “A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis”, IEMS, Vol. 4, Issue 1, June 2005, pp. 102-108.
[11]Tsirogiannis, G.L. Frossyniotis, D. Stoitsis, J. Golemati, S. Stafylopatis, A. Nikita, K.S., “Classification of medical data with a robust multi-level combination scheme” in Proceeding of 2004 IEEE International Joint Conference on Neural Networks, Vol. 3, 25-29 July 2004, pp. 2483- 2487.
[12]R.E. Abdel-Aal, “Improved classification of medical data using adductive network committees trained on different feature subsets”, Computer Methods and Programs in Biomedicine, Volume 80, Issue 2, 2005, pp. 141-153.
[13]Gongqing Wu, Haiguang Li, Xuegang Hu, Yuanjun Bi, Jing Zhang and Xindong Wu “MReC4.5: C4.5 ensemble classification with MapReduce”, in Proceeding of 2009 Fourth ChinaGrid Annual Conference, 2009, pp. 249-255.
[14]Seppo Puuronen and Vagan Terziyan “A Similarity Evaluation Technique for Data Mining with an Ensemble of classifiers”, Cooperative Information Agents III, Third International Workshop, CIA, 1999, pp. 163-174.
[15]Lei Chen and Mohamed S. Kamel “New Design of Multiple Classifier System and its Application to the time series data” IEEE International Conference on Systems, Man and Cybernetics, 2007, pp. 385-391.
[16]Kai Jiang, Haixia Chen, Senmiao Yuan “Classification for Incomplete Data Using Classifier Ensembles”, Neural Networks and Brain, 2005.