ENDORSEMENT OF SMALL PATIENTS POPULATION STUDY THROUGH DATA MINING CLASSIFICATION: SIGNIFICANCE TO MANIFEST DRUG INTERACTION STUDY OF CARDIOVASCULAR DOSAGE FORMULATION
Keywords:Seven Classifiers, computatIonal statistical analysis, Physio-chemical patients data, Data mining process
Objective: A simple, sensitive, precise computational classifiers justifies the positive indication of drug interaction through statistical validation and confirms for further root level investigation.
Methods: The blood pressure (BP) & Lipid profile valued data sheet was prepared from 100 patients those were chronically treating with cardiovascular formulation consisting Atorvastatin 10mg + Olmesartan 20mg. The data sheet contains 100 patients with 10 variables and final decision attributes of working & non-working. Then, with the operation of seven different related classifier the details of % of accuracy by class, correct & incorrect classified instance and stratified cross- validation were estimated. Those statistical results of classifiers were compared, correlate and interpreted to bring a fixed conclusion based on it.
Results: The % of accuracy for all classifiers results commonly 95.9596 %, 93.9394 % and 96.9697 % and inter-depending class attributes denoting by a = NW & b =W Matrix values are 84â”‚11, 84â”‚9, 87â”‚9 respectively. Thus, the accuracy is excellent covering within the limits of (Â±15%) as a correct classified instant.
Conclusion: Statistical computation on less populated patients through classifiers, evidentially confirms the drug-interaction profile of collected data through data mining process. So that, it can proceeds further upto root level through instrumental bioanalysis.
Baesens B, Egmont-Petersen M, Castelo R, Vanthienen J. Learning Bayesian network classifier for credit scoring using Markov chain Monte Carlo serach Proceedings. Int Congress on Pattern Recognition 2002;3:49-52.
Brent M. Instance-Based learning:Nearest neighbor with generalization. Masterâ€™s thesis at the university of Waikato, New Zealand;1995. p. 1-76.
Davis DN, Nguyen TT. Generating and verifying risk prediction models using data mining:A case studyfrom Cardiovascular medicine. Chapter of data mining and medical knowledge management:Cases and applications, ISBN10:1605662186. J IGI Global Inc 2009.
Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. J Al Magazine 1996;17(3):37-45.
Garofalakis M, Hyun D., Rastogi R, Shim K. Building decision trees with constraints. J Data Mining and Knowledge Discovery 2003;7(2):187-214.
Mitchell T M. Machine learning. Mc Graw-Hill Companies. In USA 1997;414.
Nilson NJ. Introduction to machine learning. Unpublished draft;In Standford University, USA, 1996.
Palaniappan S, Awang R. Intelligent heart Disease prediction system using data mining Techniques. Int J of Computer Sc and Network Security 2008;8(8):343-50.
American Cancer Society. Breast Cancer Facts & Figures 2005-2006. Atlanta:American Cancer Society. J Inc
Houston, Andrea L. and Chen, et al. Medical Data Mining on the Internet:Research on a Cancer Information System. J Artificial Intelligence Rev 1999;13:437-66.
Cios KJ, Moore GW. Uniqueness of medical data mining. J Artificial Intelligence in Medicine 2002;26:1-24.
Zhou ZH, Jiang Y. Medical diagnosis with C4.5Rule preceded by artificial neural network ensemble. J IEEE Trans Inf Technol Biomed 2003;7(1):37-42.
Lundin M, Lundin J, Burke HB, Toikkanen S, Pylkkanen L, Joensuu H. Artificial neural networks applied to survival prediction in breast cancer. J Oncology 1999;57: 281-6.
Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. J Artificial Intelligence in Medicine 2005;34(2):113-27.
Holmes JH, Durbin DR, Winston FK. Discovery of predictive models in an injury surveillance database: An application of data mining in clinical research. J Proc AMIA Symp 2000;359-63.
Downs SM, Wallace MY. Mining Association rules from a pediatric primary care decision support system. J Proc AMIA Symp 2000;200-04.
Brossette SE, Sprague AP, Hardin JM, Waites KB, Jones WT, Moser SA. Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inform Assoc 1998;5,:373-81.
Prather JC, Lobach DF, Goodwin LK, Hales LK, Hage ML, Hammond WE. Medical data mining: Knowledge discovery in a clinical data warehouse. Proc AMIA Symp 1997;101-05.
John Hayward. Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients. A Thesis submitted to Worcester Polytechnical Institute, Aug. 2006, MA 01609, United States.
Hosking JR, Pednault EP, Sudan M. Statistical perspective on data mining. J Future Generaltion Computer System. 1997;13(3):117-34.
Keim DA, Mansmann F, Schneidewind J, Ziegler H. Challenges in visual data analysis.
Information Visualization. DOI:10.1109/IV.2006.31, 10th Int Conference 2006;9-16.
Mannila H. Data mining: machine learning, statistics and databases. Paper presented at:8th J Int Conference on Scientific and Statistical Database Systems 1996.
Bohacik J, Darryl ND. Estimation of cardiovascular patient risk with a Bayesian network. J Transcom 2011;27:129-32.