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Exploring on Various Prediction Model in Data Mining Techniques for Disease Diagnosis

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 77 - Number 5
Year of Publication: 2013
K. Lokanayaki
A. Malathi

K Lokanayaki and A Malathi. Article: Exploring on Various Prediction Model in Data Mining Techniques for Disease Diagnosis. International Journal of Computer Applications 77(5):26-29, September 2013. Full text available. BibTeX

	author = {K. Lokanayaki and A. Malathi},
	title = {Article: Exploring on Various Prediction Model in Data Mining Techniques for Disease Diagnosis},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {77},
	number = {5},
	pages = {26-29},
	month = {September},
	note = {Full text available}


The main objective of this survey paper focused on variety of data mining techniques, approaches and different researches which are ongoing and helpful to medical diagnosis of disease. The survey is conducted in three different dimensions. Study was conducted using classification model, clustering model and bio-inspirational model. The study reveals that depending on the type of dataset used each model differs in their performance. For predicting the disease with labeled dataset the classification model was well suited in that the support vector machine and its variants are highly used. If the dataset consist of unlabelled features then the clustering model better suits for pattern recognition among the several methods k-means algorithm with the improvisation is adapted by researches due to its simplicity. To increase the performance of dataset with more optimization, then the bio-inspirational based techniques is well suited, in this particle swarm optimization is most used because of its bigger optimization ability and it can be completed easily. Thus the paper investigates the importance of each model in the field of medical diagnosis.


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