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
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.
Keywords – KNN Algorithm, Health Examination Record, Unlabeled Data
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