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Multiple-Time-Series Clinical Data Processing for Classification: A Review

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Priyanka Raj, Surya S. R.
10.5120/ijca2016908105

Priyanka Raj and Surya S R.. Article: Multiple-Time-Series Clinical Data Processing for Classification: A Review. International Journal of Computer Applications 133(13):1-3, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Priyanka Raj and Surya S. R.},
	title = {Article: Multiple-Time-Series Clinical Data Processing for Classification: A Review},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {13},
	pages = {1-3},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Data mining is a multidisciplinary subfield of computer science. It is used in various fields such as medical research, financial, telecommunication, scientific application. Classification is a method used in data mining. Data mining includes wide varieties of data such as clinical, scientific, biological, remote sensing etc. Clinical data can be used for clinical data mining. Clinical data mining helps the clinicians for diagnosis, therapy and prognosis of various diseases. Most popular primary liver cancer is hepatocellular carcinoma (HCC). It is the fifth most common tumour in the world. HCC can be treated by using Radiofrequency ablation (RFA). Recurrence prediction of hepatocellular carcinoma (HCC) after RFA treatment is an important task. This problem can be solved by using a classification technique that classifies persons into two groups: 1) HCC recurrence and 2) no evidence of recurrence of HCC. In this paper a review is being carried out in various techniques used in HCC recurrence prediction are discussed.

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Keywords

Clinical data mining, Hepatocellular Carcinoma (HCC), Radiofrequency Ablation (RFA)