IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods

Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods
View Sample PDF
Author(s): Vyshali J. Gogi (Rashtreeya Vidyalaya College of Engineering, India) and Vijayalakshmi M. N. (Rashtreeya Vidyalaya College of Engineering, India)
Copyright: 2021
Pages: 9
Source title: Encyclopedia of Information Science and Technology, Fifth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3479-3.ch058

Purchase

View Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods on the publisher's website for pricing and purchasing information.

Abstract

Hepatocellular carcinoma (HCC) is an abnormal condition of human liver which is diagnosed at a very advanced stage. The disease is liver disorder which can be predicted after series of clinical and laboratory and imaging studies. Hepatocellular carcinoma is the most malignant tumors which is the major cause of death and requires to be treated at early stage. The prognosis of liver disease is the reflection of both tumor characteristics like tumor size, location and tumor biology along with the degree of underlying resection. Healthcare domain generates huge data which is very complex and vast. The data contains many hidden parameters and patterns which is useful in predicting the disease. Data mining helps in recognizing these hidden patterns and arriving at the diagnosis of the disease. In this chapter the authors are concentrating on HCC tumor makers. The aim of the study is to use data mining techniques to predict the presence of tumor markers and their contribution in HCC Progression.

Related Content

Yair Wiseman. © 2021. 11 pages.
Mário Pereira Véstias. © 2021. 15 pages.
Mahfuzulhoq Chowdhury, Martin Maier. © 2021. 15 pages.
Gen'ichi Yasuda. © 2021. 12 pages.
Alba J. Jerónimo, María P. Barrera, Manuel F. Caro, Adán A. Gómez. © 2021. 19 pages.
Gregor Donaj, Mirjam Sepesy Maučec. © 2021. 14 pages.
Udit Singhania, B. K. Tripathy. © 2021. 11 pages.
Body Bottom