IJMTES – DATA FUSION FOR HEALTH EXAMINATION RECORD TO BE INTEGRATED WITH MULTIPLE DATA SETS

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : DATA FUSION FOR HEALTH EXAMINATION RECORD TO BE INTEGRATED WITH MULTIPLE DATA SETS

Author’s Name : P Priyanka | D Raghuraman
unnamed

Volume 04 Issue 03 2017

ISSN no:  2348-3121

Page no: 188-189

Abstract –Distinctive the participants in hazard are most important for early Notice and preventive intervention. The challenge of learning a classification model for risk forecast lies within the unlabeled knowledge that establishes the bulk of the collected data set. Significantly, the unlabeled knowledge describes the contributors in health investigations whose health conditions will vary greatly from healthy to very-ill. KNN algorithm for risk predictions to categorize an increasingly developing scenario with the bulk of the information unlabeled Wide-ranging experiments supported each health examination data sets.

KeywordsKNN Algorithm, Health Examination Record, Unlabeled Data

Reference

  1. M. F. Ghalwash, V. Radosavljevic, and Z. Obradovic, “Extraction of interpretable multivariate patterns for early diagnostics,” IEEE International Conference on Data Mining, pp. 201–210, 2013.
  2. T. Tran, D. Phung, W. Luo, and S. Venkatesh, “Stabilized sparse ordinal regression for medical risk stratification,” Knowledge and Information Systems, pp. 1–28, Mar. 2014.
  3. M. S. Mohktar, S. J. Redmond, N. C. Antoniades, P. D. Rochford,J. J. Pretto, J. Basilakis, N. H. Lovell, and C. F. McDonald, “Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home tele health measurement data, “Artificial Intelligence in Medicine, vol. 63, no. 1, pp. 51–59, 2015.
  4. J. M. Wei, S. Q. Wang, and X. J. Yuan, “Ensemble rough hyper cuboid approach for classifying cancers,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 3, pp. 381–391, 2010.
  5. E. Kontio, A. Airola, T. Pahikkala, H. Lundgren-Laine, K. Junttila,H. Korvenranta, T. Salakoski, and S. Salanter¨a, “Predicting patient acuity from electronic patient records.” Journal of Biomedical Informatics,vol. 51, pp. 8–13, 2014.