Abstract
The medical databases are expanding at exponential rate; forfeit technology is required to determine hidden information and facts from such big databases. The data mining technology or Knowledge discovery from databases (KDD) tends to be the well-known technique from past decade which has provided fruitful information to discover hidden patterns and unknown knowledge from large-scale databases. Further, as medical databases are exceeding at an enormous rate data mining tends to be an effective and efficient technology to deal with inconsistent databases which include missing value, noisy attributes, and other types of attributes or factors to discover knowledgeable information for future prognosis of disease. In the current study, we have utilized predictive data analytics technique to diagnose patients suffering from liver disorder. The paper utilizes two-step clustering technology to analyze patients’ disorder with different data variables to find optimal number of clusters of variant shapes and sizes. The focus of study relies on determining hidden knowledge and important factors which can benefit healthcare practitioners and scientists around the globe for prognosis and diagnosis of liver disease at an early stage.
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References
Kiruba, H.R., Arasu, G.T.: An intelligent agent based framework for liver disorder diagnosis using artificial intelligence techniques. J. Theor. Appl. Inf. Technol. 69(1) (2014)
Tsumoto: Problems with Mining Medical Data. 0-7695- 0792-1 I00@2000 IEEE
Aslandogan et. al. A.Y.: Evidence combination in medical data mining. In: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04) 0-7695-2108-8/04©2004 IEEE
Ordonez C.: Improving heart disease prediction using constrained association rules. Seminar Presentation at University of Tokyo (2004)
Le Duff, F., Munteanb, C., Cuggiaa, M., Mabob, P.: Predicting survival causes after out of hospital cardiac arrest using data mining method. Stud. Health Technol. Inf. 107(Pt 2), 1256–1259 (2004)
Szymanski, B., Han, L., Embrechts, M, Ross, A., Zhu, K.L.: Using efficient supanova kernel for heart disease diagnosis. In: Proceedings of the ANNIE 06, Intelligent Engineering Systems Through Artificial Neural Networks, vol. 16, pp. 305–310 (2006)
https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset)
Vaziraniet H. et al.: Use of modular neural network for heart disease. In: Special Issue of IJCCT 2010 for International Conference (ACCTA-2010), Vol.1 Issue 2, 3, 4, pp. 88–93, 3–5 Aug 2010
Chaitrali, S., Dangare et. al.: Improved study of heart disease prediction system using data mining classification techniques. (IJCA) (0975–8887), 47(10), 44–48 (2012)
Lin, R.H.: An Intelligent model for liver disease diagnosis. Artif. Intell. Med. 47(1), 53–62 (2009)
Lee, H.G., Noh K.Y., Ryu K.H.: Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. In: LNAI 4819: Emerging Technologies in Knowledge Discovery and Data Mining, May 2007, pp. 56–66
Guru, N., Dahiya A., Rajpal N.: Decision support system for heart disease diagnosis using neural network. Delhi Bus. Rev. 8(1) (2007)
Wang, H.: Medical knowledge acquisition through data mining. In: Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education 978-1-4244-2511-2/08©2008 Crown
Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. (IJCSNS) 8(8) (2008)
Parthiban, L., Subramanian, R.: Intelligent heart disease prediction system using CANFIS and genetic algorithm. Int. J. Biol. Biomed. Med. Sci. 3, 3 (2008)
Jin, H., Kim, S., Kim, J.: Decision factors on effective liver patient data prediction. Int. J. Bio-Sci. Bio-Technol. 6(4), 167–168 (2014)
Chauhan, R., Jangade, R.: A robust model for big healthcare analytics. In: Confluence (2016)
Jangade, R., Chauhan, R.: Big data with integrated cloud computing for healthcare analytics. In: INDIACOM (2016)
Glymour, C., Madigan, D., Pregibon, D., Smyth, P.: Statistical inferenceand data mining. Commun. ACM 39(11), 35–41 (1996)
Han, J.W., Yin, Y., Dong, G.: Efficient mining of partial periodic patterns in time series database. IEEE Trans. Knowl. Data Eng. (1998)
Kaur, H., Chauhan, R., Alam, M.A.: SPAGRID: A spatial grid framework for medical high dimensional databases. In: Proceedings of International Conference on Hybrid Artificial Intelligence Systems, HAIS 2012, springer, vol. 1, pp. 690–704 (2012)
Kaur, H., Chauhan, R., Aljunid, S.: Data Mining Cluster analysis on the influence of health factors in Casemix data. BMC J. Health Serv. Res. 12(Suppl. 1), O3 (2012)
Chauhan, R., Kaur, H.: Predictive analytics and data mining: a framework for optimizing decisions with R tool. In: Tripathy, B.,: Acharjya, D. (eds.) Advances in Secure Computing, Internet Services, and Applications. Hershey, PA, Information Science Reference, pp. 73–88 (2014). https://doi.org/10.4018/978-1-4666-4940-8.ch004
Kaur, H., Chauhan, R., Alam, M.A.: Data clustering method for discovering clusters in spatial cancer databases. Int. J. Comput. Appl. 10(6), 9–14 (2010)
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Chauhan, R., Kumar, N., Rekapally, R. (2019). Predictive Data Analytics Technique for Optimization of Medical Databases. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_40
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