In current era, the trend of application of data mining is widely used because of health sector is rich in information and data mining has become its necessity. In the healthcare organizations many data and information is generated on daily basis. Use of data mining and knowledge that help bring some interesting patterns which means eliminate manual tasks and easy data extraction from any electronic records, through that will secure medical records, save patient’s lives and also reduce the cost of medical services as well as early detection of any infectious disease on the basis of historical and advanced data collection. Data mining can enable healthcare organizations to predict trends in the patient’s medical condition and behavior proved by analysis of different prospects and by making connections between totally unrelated data and information. Generally the raw data from the healthcare organizations are tremendous and heterogeneous. These all data can be gathered from various sources or different components. Data mining has great importance for area of healthcare and also it represents comprehensive process that demands through understanding of requirement of the healthcare organization. Knowledge gained with the use of techniques of data mining can be used to make successful decisions that will improve success of healthcare organizations and also health of the patients. Data mining once started, represents continuous cycle of knowledge discovery. In this paper, I wish to discuss that how data mining is used in infectious disease like cervical cancer.

Data Mining, Knowledge Discovery Database, Cervical cancer, Classification, Clustering

[1]. Eapen, A. G. (2004). “Application of Data mining in Medical Applications”.Ontario, Canada, 2004: University of Waterloo.
[2]. Chen M. S., Han J., and Yu P. S., (1996), ‘Data Mining: An Overview from Database Perspective’, IEEE Transactions on Knowledge and Data Engineering, Vol.8, No.6, pp.866-883.
[3]. Cios K. J. (ed.), (2000), ‘Medical Data Mining and Knowledge Discovery”, Physica-Verlag (Springer).
[4]. Divya Tomar, Sonali Agarwal (2013), “A survey on Data Mining approaches for Healthcare” International Journal of Bio-Science and Bio-Technology, Vol-5, No-5, pp.241-266 (ISSN 2233-7849)
[5]. Illhoi Yoo, Patricia Alafaireet, Miroslav Marinov, Keila Pena-Hernandez, Rajitha Gopidi, Jia-Fu Chang, Lei Hua (2011) “Data Mining in Heath Care and Biomedicine: A survey of the literature” J Med Syst DOI 10.1007/s 10916-011-9710-5, Springer.
[6]. J.Han and M.Kamber(2006), “Data Mining: Concept and techniques”,2nd edition, The Morgan Kaufmann Series.
[7]. Brachman, R. J. & Anand, T. (1996), “The process of knowledge discovery in databases.” AAAI Press / The MIT Press.
[8]. Fayyad U.M. et al. (1996), “Data Mining and Knowledge Discovery: making sense out of data ”,IEEE Expert, Vol-11, No-5, pp. 20-25.
[9]. Kasper, Fauci, Hauser et al. (2015). Part-7: Oncology and Hematology. 19th Edition Harrion’s Principles of Internal Medicine. McGraw Hill Education: New York. P-467
[10]. https://www.cancer.org/cancer/cervical-cancer/about/what-is-cervical-cancer.html
[11]. Denny L. (2012) “Cervical cancer: prevention and treatment.” Discov Med. 14: PP-125–131.
[12]. Arbyn M, Castellsague X, DeSanjose S, et al. (2011) “ Worldwide burden of cervical cancer. Ann Oncol.” 22: PP- 2675–2686.
[13]. Yeole BB, Kumar AV, Kurkureet A, Sunny L (2004). “Population-based survival from cancers of breast, cervix and ovary in women in Mumbai.” Asian Pac J Cancer Prev. 5:308–315.
[14]. ICO Information Centre on HPV and cancer (Summary Report 2014-08-22).Human Papillomavirus and Related Diseases in India. 2014
[15]. PrashantNaresh, (Aug-2014). “Early Detection of Lung Cancer Using Neural Network Techniques”, Journal of Engineering Research and Applications, Vol-4, Issue-8.
[16]. Ravi Kumar, G., Ramachandra.A, Nagamani.K, (Aug-2013). “An Efficient Prediction of Breast Cancer Data Using Data Mining Techniques”, International Journal of Innovations in Engineering and Technology (IJIET) Vol-2, Issue-4.
[17]. K.Srinivas, B. Kavitha Rani and Dr. A. Govrghan (2010), “Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks” International Journal on Computer Science and Enginerring, Vol-2,No-2,pp.250-255.
[18]. A. Sudha (March-2012). “Utilization of Data Mining Approaches for Predictin of Life Threatening Diseases Survivability”, International Journal of Computer Applications (0975-8887) Vol-41-No. 17.
[19]. Neha Sharma (Sept-2012). “Framework for Early Detection and Prevention of oral Cancer Using Data Mining” International Journal of Advances in Engineering and Information Technology (IJAET) ISSN: 2231-1963, Vol-4, Issue-2, pp. 302-310.
[20]. Blake, C., & Merz, C.J. (2001) UCI Repository of Machine Learning Databases. [Machine-readable data repository]. University of California, Department of Information and Computer Information and Computer Science, Irvine, C.A. [Available from http://www.ics.uci.edu/~mlearn/MLRepository.html]
[21]. Sushmita Mitra, Pabitra Mitra(July-2000). “Staging of Cervical Cancer with Soft Computing”, IEEE Transactions on BioMedical Engineering, Vol-47, No-7.
[22]. C.McGregor, C.Christina and J. Andrew (2012), “ A process mining driven framework for clinical guideline improvement in critical care”, Learning from Medical Data Streams 13th Conference on Artificial Intelligence in Medicine (LEMEDS). http://ceur-ws.org, vol-765.
[23]. V. Vapnik (1998). “The support vector method of function estimation”
[24]. N. Chistianini and J. Shawe-Taylor (2000), “An Introduction to Support Vector Machines, and other Kernel-based learning methods”, Cambridge University Press.
[25]. Apte & S.M. Weiss (1997), “Data Mining with Decision Trees and Decision Rules”, T.J. Watson Research Center, http:// www.research.ibm.com/dar/papers/pdf/fgcsapteweissue_with_cover.pdf.
[26]. Kaur, H., and Wasan, S.K. (2006), “Empirical study on applications of data mining techniques in healthcare”, I.comput. Sci. 2(2), pp. 194-200.
[27]. Bellazzi, R, and Zupan, B.(2008), “Predictive data mining in clinical medicine: current issues and guidelines”, Int. J. Med. Inform. 77:pp. 81-97.
[28]. Ubeyli, E.D.(2007),” Comparison of different classification algorithms in clinical decision making”, Expert syst 24(1): pp.17-31.
[29]. Potter, R., (july-2007), “Comparison of classification algorithms applied to breast cancer diagnosis and prognosis, advances in data mining”, 7th Industrial Conference, ICDM- 2007, Leipzig, Germany, pp.- 40-49.
[30]. Romeo, M., Burden, F., Quinn, M., Wood, B., and McNaughton, D. (1998), “Infrared microspectroscopy and artificial neural networks in the diagnosis of cervical cancer”, Cell. Mol. Biol. (Noisy –le-Grand, France) 44(1): 179.
[31]. Brickly, M., Shepherd, J. P., and Armstrong, R.A.(1998), “Neural networks: a new technique for development of decision support systems in dentistry”, J.Dent. 26(4): pp. 305-309.
[32]. Einstein, A. J., Wu, H. S., Sanchez, M., and Gil, J.(1998), “fractal characterization of chromatin appearance for diagnosis in breast cytology”, J. Pathol. 185(4): pp. 366-381.
[33]. J. Fox (1997), “Applied Regression Analysis, Linear Models, and Related Methods”.
[34]. U. Fayyad, G. Piatetsky- Shapiro and P. Smyth (1996), “The KDD process of extracting useful knowledge from volumes of data. Commun.”, ACM, Vol-39, no-11,pp. 27-34.
[35]. Agrawal, R., Imielinski, T., and Swami, A.(1993), “Mining association rules between sets of items in large databases” , Proceedings of the ACM SIGMOD International Conference on the Management of Data. ACM, Washington DC, pp. 207-216.
[36]. Agrawal, R., and Srikant, R.(1994), “Fast algorithm for mining association rules”, Proceedings of the 20th international Conference on Very Large Data Bases (VLDB’94). Morgan Kaufmann, Santiago, pp. 487-499.