Papers

Title: Improving Prediction Accuracy of Disease Prediction using Hybrid Approach of KNN and Euclidean Distance
Year of Publication: 2017
Publisher: International Journal of Computer Systems (IJCS)
ISSN: 2394-1065
Series: Volume 04, Number 5, May 2017
Authors: Amandeep Kaur , Varinder Kaur Attri

Citation:

Amandeep Kaur , Varinder Kaur Attri , "Improving Prediction Accuracy of Disease Prediction using Hybrid Approach of KNN and Euclidean Distance", In International Journal of Computer Systems (IJCS), pp: 105-108, Volume 4, Issue 5, May 2017. BibTeX

@article{key:article,
	author = {Amandeep Kaur , Varinder Kaur Attri},
	title = {Improving Prediction Accuracy of Disease Prediction using Hybrid Approach of KNN and Euclidean Distance},
	journal = {International Journal of Computer Systems (IJCS)},
	year = {2017},
	volume = {4},
	number = {5},
	pages = {105-108},
	month = {May}
	}


Abstract

Health monitoring is critical issue associated with now day lifestyle. Lack of time is causing serious issues corresponding to health. Proposed literature focus on this key aspect and provide mechanism to generate accurate predictions corresponding to parameters fetched from dataset. Hybrid approach of K Nearest neighbour and Euclidean distance is used for enhancement in health prediction. For demonstration dataset derived from UCI is utilized. Simulation results suggest considerable improvement over KNN and Euclidean distance mechanism during prediction.

References

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Keywords

Health Monitoring, Prediction, dataset, K nearest neighbour, Euclidean distance, UCI.