A Comparative Study of Data Mining, Digital Image Processing and Genetical Approach for Early Detection of Liver Cancer

Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. The largest internal organ of our body is the liver. It performs many of the life sustaining functions and elementarily has effect on every physiological process occurring in our body. Liver is the second most organ which is metastatistically affected by cancer. Now a days, liver cancer becomes the main cause of death worldwide. At that time, the early prediction of liver cancer becomes necessary for increasing patient survival. In this paper, a comparison is made to know which method from genetic, data mining and digital image processing is best suited for early detection of liver cancer. After doing comparison, in this paper we decide to explore genetical approach. All the statistical methods work on microarray expression dataset and come under the category of genetical approach. Microarray data analysis evolved as an advanced research topic during recent decades. The key reason for analyzing microarray dataset is to identify and predict the gene that displays various values of expression under various testing conditions. The purpose of this research paper is to examine various forms of parametric statistical methods proposed to analyze data on microarray expression to classify the genes expressed differently and to investigate the mechanism of disease growth. Additionally, we have estimated the best situation for each system where they are doing better. In our research, we use the simulated datasets to thoroughly analyze and compare the various types of parametric methods.